APPLICATIONS OF DEEP LEARNING AND MACHINE LEARNING IN HEALTHCARE DOMAIN – A LITERATURE REVIEW

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International Journal of Electrical Engineering and Technology (IJEET)
Volume 11, Issue 8, October 2020, pp. 113-126, Article ID: IJEET_11_08_011
Available online at https://iaeme.com/Home/issue/IJEET?Volume=11&Issue=10
ISSN Print: 0976-6545 and ISSN Online: 0976-6553
https://doi.org/10.34218/IJEET.11.8.2020.011
© IAEME Publication Scopus Indexed
APPLICATIONS OF DEEP LEARNING AND
MACHINE LEARNING IN HEALTHCARE
DOMAIN – A LITERATURE REVIEW
Dr. M. Lalli
Assistant Professor in Computer Science, Bharathidasan University,
Tiruchirappalli, Tamil Nadu, India
S. Amutha
Research Scholar in Computer Science, Bharathidasan University,
Tiruchirappalli, Tamil Nadu, India
ABSTRACT
Artificial intelligence (AI) has been developing rapidly in recent years in terms of
software algorithms, hardware implementation, and applications in a vast number of
areas. In this review, we summarize the latest developments of applications of AI in
biomedicine, including disease diagnostics, living assistance, biomedical information
processing, and biomedical research. Various automated systems and tools like Brain-
computer interfaces (BCIs), arterial spin labelling (ASL) imaging, ASL-MRI,
biomarkers, Natural language processing (NLP) and various algorithms helps to
minimize errors and control disease progression. The computer assisted diagnosis,
decision support systems, expert systems and implementation of software may assist
physicians to minimize the intra and inter-observer variability. In this paper, a detailed
literature review on application and implementation of Machine Learning, Deep
Learning and Artificial Intelligence in the healthcare industry by various researchers.
Keywords: Healthcare, Machine Learning, Deep Learning, Artificial Intelligence,
Disease Severity, Survival prediction, Big data
Cite this Article: M. Lalli and S. Amutha, Applications of Deep Learning and Machine
Learning in Healthcare Domain – A Literature Review, International Journal of
Electrical Engineering and Technology, 11(8), 2020, pp. 113-126.
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1. INTRODUCTION
Artificial intelligence (AI) [1] is defined as the intelligence of machines, as opposed to the
intelligence of humans or other living species. AI can also be defined as the study of ‘‘intelligent
agents”—that is, any agent or device that can perceive and understand its surroundings and
accordingly take appropriate action to maximize its chances of achieving its objectives. AI also

Applications of Deep Learning and Machine Learning in Healthcare Domain – A Literature Review
refers to situations wherein machines can simulate human minds in learning and analysis, and
thus can work in problem solving. This kind of intelligence is also referred to as machine
learning (ML) [2].
We are living in the age of algorithms, in which machine learning (ML)/deep learning (DL)
systems have transformed multiple industries such as manufacturing, transportation, and
governance. Over the past few years, DL has provided state of the art performance in different
domains—e.g., computer vision, text analytics, and speech processing, etc. Due to the extensive
deployment of ML/DL algorithms in various domains (e.g., social media), such technology has
become inseparable from our routine life. ML/DL algorithms are now beginning to influence
healthcare as well—a field that has traditionally been impervious to large-scale technological
disruptions [3]. ML/DL techniques have shown outstanding results recently in versatile tasks
such as recognition of body organs from medical images, classification of interstitial lung
diseases, detection of lungs nodules, medical image reconstruction, and brain tumor
segmentation, to name a few [4]. It is highly expected that intelligent software will assist
radiologists and physicians in examining patients soon and ML will revolutionize the medical
research and practice. Clinical medicine has emerged as an exciting application area for ML/DL
models, and these models have already achieved human-level performance in clinical
pathology, radiology, ophthalmology, and dermatology [5].
The potential of ML models for healthcare applications is also benefitting from the progress
in concomitantly advancing technologies like cloud/edge computing, mobile communication,
and big data technology [6]. Together with these technologies, ML/DL can produce highly
accurate predictive outcomes and can facilitate the human-centered intelligent solutions [7].
Along with other benefits like enabling remote healthcare services for rural and low-income
zones, these technologies can play a vital role in revitalizing the healthcare industry.
2. MACHINE LEARNING IN HEALTHCARE
The major phases for developing a ML-based healthcare system are illustrated and major types
of ML/DL that can be used in healthcare applications are briefly described next.
2.1. Un-Supervised Machine Learning
The ML techniques utilizing unlabelled data are known as unsupervised learning methods.
Widely used examples of unsupervised learning methods are a clustering of data points using a
similarity metric and dimensionality reduction to project high dimensional data to lower-
dimensional subspaces (sometimes also referred to as feature selection). In addition,
unsupervised learning can be used for anomaly detection, e.g., clustering [8]. Classical
examples of unsupervised learning methods in healthcare and prediction of hepatitis disease
using principal component analysis (PCA) which is a dimensionality reduction technique [9].
2.2. Supervised Machine Learning
Such methods that build or map the association between the inputs and outputs using labeled
training data are characterized as supervised learning methods [10]. If the output is discrete then
the task is called classification and for a continuous value output, the task is called regression.
Classical examples of supervised learning methods in healthcare include the classification of
different types of lung diseases (nodules) and recognition of different body organs from medical
images. Sometimes, ML methods can be neither supervised nor unsupervised, i.e., where the
training data contains both labeled and unlabelled samples. Methods utilizing such data are
known as semi-supervised learning methods.

M. Lalli and S. Amutha
2.3. Semi-Supervised Machine Learning
Semi-supervised learning methods are useful when both labelled and unlabelled samples are
available for training, typically, a small amount of labelled data and a large amount of
unlabelled data. Semi-supervised learning techniques can be particularly useful for a variety of
healthcare applications as acquiring enough labelled data for model training is difficult in
healthcare.
2.4. Reinforcement Learning
Methods that learn a policy function given a set of observations, actions, and rewards in
response to actions performed over time fall in the class of reinforcement learning (RL) [11].
RL has a great potential to transform many healthcare applications and recently, it has been
used for context-aware symptoms checking for disease diagnosis.
3. APPLICATIONS OF MACHINE LEARNING IN HEALTHCARE
Healthcare service providers generate a large amount of heterogeneous data and information
daily, making it difficult for the “traditional methods” to analyze and process it. ML/DL
methods help to effectively analyze this data for actionable insights. In addition, there are
heterogeneous sources of data that can augment healthcare data such as genomics, medical data,
data from social media, and environmental data, etc.
3.1. Applications of ML in Prognosis
Prognosis is the process of predicting the expected development of a disease in clinical practice.
It also includes identification of symptoms and signs related to a specific disease and whether
they will become worse, improve, or remain stable over time and identification of potential
associated health problems, complications, ability to perform routine activities, and the
likelihood of survival. As in clinical setting, multi-modal patients’ data is collected, e.g.,
phenotypic, genomic, proteomic, pathology tests results, and medical images, etc., which can
empower the ML models to facilitate disease prognosis, diagnosis and treatment. For instance,
ML models have been largely developed for the identification and classification of different
types of cancers, e.g., brain tumor and lung nodules. However, the potential applications ML
for disease prognosis, i.e., prediction of disease symptoms, risks, survivability, and recurrence
have been exploited under recent translational research efforts that aim to enable personalized
medicine. However, the field of personalized medicine is nascent that requires extensive
development of adjacent fields like bioinformatics, strong validation strategies, and
demonstrably robust applications of ML thus to achieve the huge and translational impact.
3.2. Applications of ML in Diagnosis
3.2.1. Electronic Health Records
Hospitals and other healthcare service providers are producing a large collection of electronic
health records (EHRs) on a daily basis and comprise of structured and unstructured data that
contains a complete medication history of patients. ML-based methods have been utilized for
the extraction of clinical features for facilitating the diagnosis process [12].
3.2.2. ML in Medical Image Analysis
In medical image analysis [13], ML techniques are used for efficient and effective extraction
of information from medical images that are acquired using different imaging modalities such
as magnetic resonance imaging (MRI), computed tomography (CT), ultrasound, and positron
emission tomography (PET), etc. These modalities provide important functional and anatomical
information about different body organs and play a crucial role in the detection/localization and

diagnosis of abnormalities. The key purpose of medical image analysis is to assist clinicians
and radiologists for efficient diagnosis and prognosis of the diseases. The prominent tasks in
medical image analysis include detection, classification, segmentation, retrieval,
reconstruction, and image registration.
3.3. Applications of ML in Treatment
3.3.1. Image Interpretation
Medical images are widely used in the routine clinical practice and the analysis and
interpretation of these images are performed by expert physicians and radiologists. To narrate
the findings regarding images being studied, they write textual radiology reports about each
body organ that was examined in the conducted study. However, writing such reports is very
challenging in some scenarios, e.g., less experienced radiologists and healthcare service
providers in rural areas where the quality of healthcare services is not up to the mark. On the
other side, for experienced radiologists and pathologists, the process of preparing high-quality
reports can be tedious and time-consuming which can be exacerbated by many patients visiting
daily. Therefore, various researchers have attempted to address this problem using natural
language processing (NLP) and ML techniques [14].
3.3.2. Real-time Health Monitoring
Real-time monitoring of critical patients is crucial and is a key component of the treatment
process. Continuous health monitoring using wearable devices, IoT sensors, and smartphones
is gaining interest among people. In a typical setting of continuous health monitoring, health
data is collected using a wearable device and smartphone and then transmitted to the cloud for
analysis using an ML/DL technique [15].
3. LITERATURE REVIEW
Almansour, Njoud Abdullah, et al [16] aimed to assist in the prevention of Chronic Kidney
Disease (CKD) by utilizing machine learning techniques to diagnose CKD at an early stage.
Kidney diseases are disorders that disrupt the normal function of the kidney. As the percentage
of patients affected by CKD is significantly increasing, effective prediction procedures should
be considered. The authors focused on applying different machine learning classification
algorithms to a dataset of 400 patients and 24 attributes related to diagnosis of chronic kidney
disease. The classification techniques used in this study include Artificial Neural Network
(ANN) and Support Vector Machine (SVM). To perform experiments, all missing values in the
dataset were replaced by the mean of the corresponding attributes. Then, the optimized
parameters for the Artificial Neural Network (ANN) and Support Vector Machine (SVM)
techniques were determined by tuning the parameters and performing several experiments. The
final models of the two proposed techniques were developed using the best-obtained parameters
and features.
Belić, Minja, et al [17] aimed to give a comprehensive, high-level overview of applications
of artificial intelligence through machine learning algorithms in kinematic analysis of
movement disorders, specifically Parkinson’s disease (PD). The authors surveyed papers
published between January 2007 and January 2019, within online databases, including PubMed
and Science Direct, with a focus on the most recently published studies. The search
encompassed papers dealing with the implementation of machine learning algorithms for
diagnosis and assessment of PD using data describing motion of upper and lower extremities.
The authors presented an overview of 48 relevant studies published in the abovementioned
period, which investigate the use of artificial intelligence for diagnostics, therapy assessment
and progress prediction in PD based on body kinematics. Different machine learning algorithms

showed promising results, particularly for early PD diagnostics. The investigated publications
demonstrated the potentials of collecting data from affordable and globally available devices.
Liang, Huiying, et al [18] showed that Machine Learning Classifiers (MLCs) can query
Electronic Health Records (EHRs) in a manner similar to the hypothetico-deductive reasoning
used by physicians and unearth associations that previous statistical methods have not found.
The proposed model applied an automated natural language processing system using deep
learning techniques to extract clinically relevant information from EHRs. The proposed model
demonstrates high diagnostic accuracy across multiple organ systems and is comparable to
experienced pediatricians in diagnosing common childhood diseases. This study provided a
proof of concept for implementing an AI-based system as a means to aid physicians in tackling
large amounts of data, augmenting diagnostic evaluations, and to provide clinical decision
support in cases of diagnostic uncertainty or complexity.
Wu, Chieh-Chen, et al [19] aimed to develop a machine learning model to predict Fatty
Liver Disease (FLD) that could assist physicians in classifying high-risk patients and make a
novel diagnosis, prevent and manage FLD. FLD is a common clinical complication; it is
associated with high morbidity and mortality. However, an early prediction of FLD patients
provides an opportunity to make an appropriate strategy for prevention, early diagnosis, and
treatment.
Jo, Taeho, Kwangsik Nho, and Andrew J. Saykin [20] performed a systematic review of
publications using deep learning approaches and neuroimaging data for diagnostic
classification of Alzheimer Disease (AD). PubMed and Google Scholar search was used to
identify deep learning papers on AD published between January 2013 and July 2018. These
papers were reviewed, evaluated, and classified by algorithm and neuroimaging type, and the
findings were summarized. Of 16 studies meeting full inclusion criteria, 4 used a combination
of deep learning and traditional machine learning approaches, and 12 used only deep learning
approaches. The combination of traditional machine learning for classification and stacked
auto-encoder (SAE) for feature selection produced accuracies of up to 98.8% for AD
classification and 83.7% for prediction of conversion from mild cognitive impairment (MCI),
a prodromal stage of AD, to AD. Deep learning approaches, such as convolutional neural
network (CNN) or recurrent neural network (RNN), that use neuroimaging data without pre-
processing for feature selection have yielded accuracies of up to 96.0% for AD classification
and 84.2% for MCI conversion prediction. The best classification performance was obtained
when multimodal neuroimaging and fluid biomarkers were combined. Deep learning
approaches continue to improve in performance and appear to hold promise for diagnostic
classification of AD using multimodal neuroimaging data.
Ngiam, Kee Yuan, and Wei Khor [21] exploited a capability such as deep neural networks,
machine learning presents a powerful tool for analysing large amounts of complex health-care
data to improve the efficiency and cost-effectiveness of health-care delivery. When used to
augment the capabilities of doctors, machine learning can perform routine, systematic tasks
with high consistency, freeing up doctors’ time so that they can address clinical problems that
are more complex or that require substantial human interaction.
Kawakami, Eiryo, et al [22] aimed to develop an ovarian cancer–specific predictive
framework for clinical stage, histotype, residual tumor burden, and prognosis using machine
learning methods based on multiple biomarkers. Overall, 334 patients with epithelial ovarian
cancer (EOC) and 101 patients with benign ovarian tumors were randomly assigned to
"training" and "test" cohorts. Seven supervised machine learning classifiers, including Gradient
Boosting Machine (GBM), Support Vector Machine, Random Forest (RF), Conditional RF
(CRF), Naive Bayes, Neural Network, and Elastic Net, were used to derive diagnostic and

prognostic information from 32 parameters commonly available from pre-treatment peripheral
blood tests and age.
Abdar, Moloud, et al [23] describe an innovative machine learning methodology that
enables an accurate detection of Coronary artery disease (CAD) and applied it to data collected
from Iranian patients. The authors first tested ten traditional machine learning algorithms, and
then the three-best performing algorithms (three types of SVM) were used in the rest of the
study. To improve the performance of these algorithms, a data pre-processing with
normalization was carried out. Moreover, a genetic algorithm and particle swarm optimization,
coupled with stratified 10-fold cross-validation, were used twice: for optimization of classifier
parameters and for parallel selection of features.
Kwon, Joon-myoung, et al [24] aimed to develop and validate deep-learning-based artificial
intelligence algorithm for predicting mortality of AHF (DAHF). DAHF predicted the in-
hospital and long-term mortality of patients with AHF more accurately than the existing risk
scores and other machine-learning models.
Kiely, David G., et al [25] explored whether a predictive model based on healthcare
resource utilisation can be used to screen large populations to identify patients at high risk of
idiopathic pulmonary arterial hypertension. Hospital Episode Statistics from the National
Health Service in England, providing close to full national coverage, were used as a measure
of healthcare resource utilisation. Data for patients with idiopathic pulmonary arterial
hypertension from the National Pulmonary Hypertension Service in Sheffield were linked to
pre-diagnosis Hospital Episode Statistics records. This study highlighted the potential
application of artificial intelligence to readily available real-world data to screen for rare
diseases such as idiopathic pulmonary arterial hypertension. This algorithm could provide low-
cost screening at a population level, facilitating earlier diagnosis, improved diagnostic rates and
patient outcomes. Studies to further validate this approach are warranted.
Makino, Masaki, et al [26] constructed a new predictive model for diabetic kidney diseases
(DKD) using AI, processing natural language and longitudinal data with big data machine
learning, based on the electronic medical records (EMR) of 64,059 diabetes patients. AI
extracted raw features from the previous 6 months as the reference period and selected 24
factors to find time series patterns relating to 6-month DKD aggravation, using a convolutional
autoencoder. AI constructed the predictive model with 3,073 features, including time series data
using logistic regression analysis. AI could predict DKD aggravation with 71% accuracy.
Shamai, Gil, et al [27] developed a machine learning model termed morphological-based
molecular profiling (MBMP). Logistic regression was used to explore correlations between
histomorphology and biomarker expression, and a deep convolutional neural network was used
to predict the biomarker expression in examined tissues.
Ding, Yiming, et al [28] used Convolutional neural network of InceptionV3 architecture
was trained on 90% of ADNI data set and tested on the remaining 10%, as well as the
independent test set, with performance compared to radiologic readers. Model was analyzed
with sensitivity, specificity, receiver operating characteristic (ROC), saliency map, and t-
distributed stochastic neighbor embedding. The authors developed and validated a deep
learning algorithm that predicts the final diagnosis of Alzheimer disease (AD), mild cognitive
impairment, or neither at fluorine 18 (18F) fluorodeoxyglucose (FDG) PET of the brain and
compare its performance to that of radiologic readers.
Shen, Jiayi, et al [29] aimed to systematically examine the literature, in particular, focusing
on the performance comparison between advanced AI and human clinicians to provide an up-
to-date summary regarding the extent of the application of AI to disease diagnoses. By doing
so, this review discussed the relationship between the current advanced AI development and
clinicians with respect to disease diagnosis and thus therapeutic development in the long run.

The authors systematically searched articles published between January 2000 and March 2019
following the Preferred Reporting Items for Systematic reviews and Meta-Analysis in the
following databases: Scopus, PubMed, CINAHL, Web of Science, and the Cochrane Library.
According to the preset inclusion and exclusion criteria, only articles comparing the medical
performance between advanced AI and human experts were considered.
Seetharam, Karthik, Sirish Shrestha, and Partho P. Sengupta [30] highlighted noteworthy
examples of machine learning utilization in echocardiography, nuclear cardiology, computed
tomography, and magnetic resonance imaging over the past year. In the past year, machine
learning (ML) has expanded its boundaries in cardiology with several positive results. Some
studies have integrated clinical and imaging information to further augment the accuracy of
these ML algorithms. The authors mentioned in this review have clearly demonstrated superior
results of ML in relation to conventional approaches for identifying obstructions or predicting
major adverse events about conventional approaches.
Das, Arun, et al [31] presented a scalable cloud based teleophthalmology architecture via
the Internet of Medical Things (IoMT) for diagnosis of Age-related Macular Disease (AMD).
In the proposed architecture, patients wear a head-mounted camera (OphthoAI IoMT headset)
to send their retinal fundus images to their secure and private cloud drive storage for
personalized disease severity detection and predictive progression analysis. A proposed AMD-
ResNet convolution neural network with 152 layers will then analyze the images to identify and
determine AMD disease severity.
Ting, Daniel Shu Wei, et al [32] provided a summary of the state-of-the-art Deep Learning
(DL) systems described for ophthalmic applications, potential challenges in clinical deployment
and the path forward. Artificial intelligence (AI) based on deep learning (DL) has sparked
tremendous global interest in recent years. DL has been widely adopted in image recognition,
speech recognition and natural language processing, but is only beginning to impact on
healthcare. In ophthalmology, DL has been applied to fundus photographs, optical coherence
tomography and visual fields, achieving robust classification performance in the detection of
diabetic retinopathy and retinopathy of prematurity, the glaucoma-like disc, macular oedema
and age-related macular degeneration. DL in ocular imaging may be used in conjunction with
telemedicine as a possible solution to screen, diagnose and monitor major eye diseases for
patients in primary care and community settings.
Wong, Zoie SY, Jiaqi Zhou, and Qingpeng Zhang [33] aimed to highlight the opportunities
gained through the use of Artificial Intelligence (AI) methods to enable reliable disease-oriented
monitoring and projection in this information age. Since the beginning of the 21st century, the
amount of data obtained from public health surveillance has increased dramatically due to the
advancement of information and communications technology and the data collection systems
now in place.
Myszczynska, Monika A., et al [34] discussed how machine learning can aid early diagnosis
and interpretation of medical images as well as the discovery and development of new therapies.
A unifying theme of the different applications of machine learning is the integration of multiple
high- dimensional sources of data, which all provide a different view on disease, and the
automated derivation of actionable insights.
Banerjee, Abhirup, et al [35] aimed to use machine learning, an artificial neural network
(ANN) and a simple statistical test to identify SARS-CoV-2 positive patients from full blood
counts without knowledge of symptoms or history of the individuals. The authors found that
with full blood counts random forest, shallow learning and a flexible ANN model predict
SARS-CoV-2 patients with high accuracy between populations on regular wards (AUC = 93-
94%) and those not admitted to hospital or in the community (AUC = 80-86%). Here AUC is
the Area Under the receiver operating characteristics Curve and a measure for model

performance. Moreover, a simple linear combination of 4 blood counts can be used to have an
AUC of 85% for patients within the community. The normalised data of different blood
parameters from SARS-CoV-2 positive patients exhibit a decrease in platelets, leukocytes,
eosinophils, basophils and lymphocytes, and an increase in monocytes.
Huang, Shigao, et al [36] reviewed the literature on the application of AI to cancer diagnosis
and prognosis and summarizes its advantages. The authors explored how AI assists cancer
diagnosis and prognosis, specifically with regard to its unprecedented accuracy, which is even
higher than that of general statistical applications in oncology. The authors also demonstrated
ways in which these methods are advancing the field. Finally, opportunities and challenges in
the clinical implementation of AI are discussed. Hence, the authors provided a new perspective
on how AI technology can help improve cancer diagnosis and prognosis, and continue
improving human health in the future.
Shen, Jiayi, et al [37] aimed to investigate the implementation of a well-performing AI
algorithm in GDM diagnosis in a setting, which requires fewer medical equipment and staff
and to establish an app based on the AI algorithm. The authors also explored possible progress
if our app is widely used. Gestational diabetes mellitus (GDM) can cause adverse consequences
to both mothers and their newborns. However, pregnant women living in low- and middle-
income areas or countries often fail to receive early clinical interventions at local medical
facilities due to restricted availability of GDM diagnosis. The outstanding performance of
artificial intelligence (AI) in disease diagnosis in previous studies demonstrates its promising
applications in GDM diagnosis.
Santo, Briana A., Avi Z. Rosenberg, and Pinaki Sarder [38] The primordial digital
pathology informatics work employed classical image analysis and machine learning to
prognosticate renal disease. Although this classical approach demonstrated tremendous
potential, subsequent advancements in hardware technology rendered artificial neural networks
‘(ANNs) the method of choice for machine vision in computational pathology’. Offering rapid
and reproducible detection, characterization and classification of kidney morphology, ANNs
have facilitated the development of diagnostic and prognostic applications. In addition, modern
machine learning with ANNs has revealed novel biomarkers in kidney disease, demonstrating
the potential for machine vision to elucidate novel pathologic mechanisms beyond extant
clinical knowledge.
Kaul, Vivek, Sarah Enslin, and Seth A. Gross [39] presents a brief historical perspective on
the evolution of AI over the last several decades and the introduction and development of AI in
medicine in recent years. A brief summary of the major applications of AI in gastroenterology
and endoscopy are also presented.
Chu, Chui S., et al [40] The natural history of oral squamous cell carcinoma (OSCC) is
complicated by progressive disease including loco-regional tumour recurrence and
development of distant metastases. Accurate prediction of tumour behaviour is crucial in
delivering individualized treatment plans and developing optimal patient follow-up and
surveillance strategies. Machine learning algorithms may be employed in oncology research to
improve clinical outcome prediction. Retrospective review of 467 OSCC patients treated over
a 19-year period facilitated construction of a detailed clinicopathological database. 34
prognostic features from the database were used to populate 4 machine learning algorithms,
linear regression (LR), decision tree (DT), support vector machine (SVM) and k-nearest
neighbours (KNN) models, to attempt progressive disease outcome prediction. Principal
component analysis (PCA) and bivariate analysis were used to reduce data dimensionality and
highlight correlated variables. Models were validated for accuracy, sensitivity and specificity,
with predictive ability assessed by receiver operating characteristic (ROC) and area under the
curve (AUC) calculation.

Kovačević, Živorad, et al [41] presented the result of application of machine learning (ML)
techniques in management of infant incubators in healthcare institutions. A total of 140 samples
was used for development of Expert system based on ML classifiers. These samples were
collected during 2015–2017 period, as part of yearly inspections of incubators in healthcare
institutions by ISO 17020 accredited laboratory. Dataset division 80–20 was used for classifiers
development and validation. Performance of the following machine learning algorithms was
investigated: Naïve Bayes (NB), Decision Tree (DT), Random Forest (RF), k-Nearest
Neighbour (kNN), and Support Vector Machine (SVM). Resulting classifiers were compared
by performance and classifier based on Decision Tree algorithm yielded highest accuracy
(98.5%) among other tested systems. Obtained results suggest that by introducing ML
algorithms in MD management strategies benefit healthcare institution firstly in terms of
increase of safety and quality of patient diagnosis and treatments, but also in cost optimization
and resource management.
Armstrong, Grayson W., and Alice C. Lorch [42] Ophthalmology as a field is uniquely
capable of capitalizing on the benefits of machine learning due to many multimodal imaging
studies and clinical metrics available. AI and machine learning have already started to
revolutionize computer-assisted diagnosis, screening, and prognostication of both anterior and
posterior segment ophthalmic disease, as is exemplified by the first FDA-approved AI
algorithm for disease diagnosis being applied to DR screening. Population-based screening
programs and telemedicine efforts stand to benefit immensely from these advances, as do
clinicians equipped with AI-based decision support tools.
Ahmed, Zeeshan, et al [43] focused on analyzing and discussing various published artificial
intelligence and machine learning solutions, approaches and perspectives, aiming to advance
academic solutions in paving the way for a new data-centric era of discovery in healthcare.
Castellazzi, Gloria, et al [44] investigated, first, whether different kinds of ML algorithms,
combined with advanced MRI features, could be supportive in classifying VD from AD and,
second, whether the developed approach might help in predicting the prevalent disease in
subjects with an unclear profile of AD or VD. Three ML categories of algorithms were tested:
artificial neural network (ANN), support vector machine (SVM), and adaptive neuro-fuzzy
inference system (ANFIS).
Aggrawal, Ritu, and Saurabh Pal [45] proposes a sequential feature selection algorithm for
detecting death events in heart disease patients during treatment to select the most important
features. Several machine learning algorithms (Linear Discriminant Analysis (LDA), Random
Forest (RF), Gradient Boosting Classifier (GBC), Decision Tree (DT), Support Vector Machine
(SVM), and K-Nearest Neighbor (KNN)) are used.
Verma, Anurag Kumar, Saurabh Pal, and Surjeet Kumar [46] presented a new method,
which applies six different data mining classification techniques and then developed an
ensemble approach using bagging, AdaBoost, and gradient boosting classifiers techniques to
predict the different classes of skin disease. Further, the feature importance method is used to
select important 15 features which play a major role in prediction. A subset of the original
dataset is obtained after selecting only 15 features to compare the results of used six machine
learning techniques and ensemble approach as on the whole dataset. The ensemble method used
on skin disease dataset is compared with the new subset of the original dataset obtained from
feature selection method.
Battineni, Gopi, et al [47] reviewed applications of machine learning (ML) predictive
models in the diagnosis of chronic diseases. Chronic diseases (CDs) are responsible for a major
portion of global health costs. Patients who suffer from these diseases need lifelong treatment.
Nowadays, predictive models are frequently applied in the diagnosis and forecasting of these
diseases. In this study, the authors reviewed the state-of-the-art approaches that encompass ML

models in the primary diagnosis of CD. This analysis covers 453 papers published between
2015 and 2019, and our document search was conducted from PubMed (Medline), and
Cumulative Index to Nursing and Allied Health Literature (CINAHL) libraries.
Ström, Peter, et al [48] aimed to develop an artificial intelligence (AI) system with clinically
acceptable accuracy for prostate cancer detection, localisation, and Gleason grading. An AI
system can be trained to detect and grade cancer in prostate needle biopsy samples at a ranking
comparable to that of international experts in prostate pathology. Clinical application could
reduce pathology workload by reducing the assessment of benign biopsies and by automating
the task of measuring cancer length in positive biopsy cores. An AI system with expert-level
grading performance might contribute a second opinion, aid in standardising grading, and
provide pathology expertise in parts of the world where it does not exist.
Feeny, Albert K., et al [49] provided the novice reader with literacy of Artificial
Intelligence/ Machine Learning (AI/ML) methods and provide a foundation for how one might
conduct an ML study. The authors provided a technical overview of some of the most
commonly used terms, techniques, and challenges in AI/ML studies, with reference to recent
studies in cardiac electrophysiology to illustrate key points. The authors highlighted an
important considerations and challenges for appropriate validation, adoption, and deployment
of AI technologies into clinical practice.
Hügle, Maria, et al [50] explained the basics of machine learning and its subfields of
supervised learning, unsupervised learning, reinforcement learning, and deep learning. The
authors provided an overview of current machine learning applications in rheumatology, mainly
supervised learning methods for e-diagnosis, disease detection, and medical image analysis.
Thomsen, Kenneth, et al [51] conducted a systematic review of existing literature,
identifying the literature through a systematic search of the PubMed database. Two doctors
assessed screening and eligibility with respect to pre-determined inclusion and exclusion
criteria. The authors presented a complete overview of artificial intelligence implemented in
dermatology. Impressive outcomes were reported in all the identified eight categories, but head-
to-head comparison proved difficult. The many areas of dermatology where the authors
identified machine learning tools indicate the diversity of machine learning.
Mayro, Eileen L., et al [52] Deep learning (DL) is a subset of artificial intelligence (AI),
which uses multilayer neural networks modelled after the mammalian visual cortex capable of
synthesizing images in ways that will transform the field of glaucoma. Autonomous DL
algorithms are capable of maximizing information embedded in digital fundus photographs and
ocular coherence tomographs to outperform ophthalmologists in disease detection. Other
unsupervised algorithms such as principal component analysis (axis learning) and archetypal
analysis (corner learning) facilitate visual field interpretation and show great promise to detect
functional glaucoma progression and differentiate it from non-glaucomatous changes when
compared with conventional software packages. Forecasting tools such as the Kalman filter
may revolutionize glaucoma management by accounting for a host of factors to set target
intraocular pressure goals that preserve vision. Activation maps generated from DL algorithms
that process glaucoma data have the potential to efficiently direct our attention to critical data
elements embedded in high throughput data and enhance our understanding of the
glaucomatous process.
Farhadian, Maryam, Parisa Shokouhi, and Parviz Torkzaban [53] aimed to design a support
vector machine (SVM) based decision-making support system to diagnosis various periodontal
diseases. Data were collected from 300 patients referring to Periodontics department of
Hamadan University of Medical Sciences, west of Iran. Among these patients, 160 were
Gingivitis, 60 were localized periodontitis and 80 were generalized periodontitis. In the
designed classification model, 11 variables such as age, sex, smoking, gingival index, plaque

index and so on used as input and output variable show the individual’s status as a periodontal
disease. Using different kernel functions in the design of the SVM classification model showed
that the radial kernel function with an overall correct classification accuracy of 88.7% and the
overall hypervolume under the manifold (HUM) value was to 0.912 has the best performance.
The results of the present study show that the designed classification model has an acceptable
performance in predicting periodontitis.
Ahn, Joseph C., et al [54] provided a comprehensive overview of hepatology-focused AI
research, discussed some of the barriers to clinical implementation and adoption, and suggest
future directions for the field.
Ahmed, Hager, et al [55] presented a real-time system for predicting heart disease from
medical data streams that describe a patient’s current health status. The main goal of the
proposed system is to find the optimal machine learning algorithm that achieves high accuracy
for heart disease prediction. Two types of features selection algorithms, univariate feature
selection and Relief, are used to select important features from the dataset. The authors
compared four types of machine learning algorithms: Decision Tree, Support Vector Machine,
Random Forest Classifier, and Logistic Regression Classifier with the selected features as well
as full features. The authors applied hyperparameter tuning and cross-validation with machine
learning to enhance accuracy. One core merit of the proposed system is able to handle Twitter
data streams that contain patients’ data efficiently.
4. PROBLEM STATEMENT
The accurate prediction of survival rate in patients with diseases remains a challenge due to the
increasing complexity of disease, treatment protocols, and various patient population samples.
Reliable and well-validated predictions could assist in a better way personalized care and
treatment and improve the control over the cancer development. There is a definite increase in
the use of classification-based approaches in contemporary medical diagnostics. At first sight,
all these classification-based approaches use various and heterogeneous medical data and can
inflate the quality of diagnostics. On the contrary, numerous recent developments in computer
science, data science, and ML assist in the decrease of errors in overall diagnostics. The use of
artificial intelligence techniques for classification in medical studies provides more informative
knowledge-based background for prediction and prognosis of disease to be tested more
meticulously and rapidly, in a short time.
5. RESEARCH DIRECTION
There is an intensive and rapid development of new knowledge-based diagnostic methods for
disease detection with the extended use of tools of bioinformatics, computer science, statistics,
and machine learning. Aside from that, many of these methods are difficult for integration and
combination in a meaningful workflow. With the advent of a large development and application
of machine learning (ML) methods in the medical studies, they have become more accurate and
based on the discovery of new enriched knowledge about origin, classification, prognosis, and
therapy.
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