Detection of myocardial infarction on recent dataset using machine learning

International Journal of Informatics and Communication Technology (IJ-ICT)
Vol. 11, No. 1, April 2022, pp. 20~31
ISSN: 2252-8776, DOI: 10.11591/ijict.v11i1.pp20-31  20
Journal homepage: http://ijict.iaescore.com
Detection of myocardial infarction on recent dataset using
machine learning
Nusrat Parveen1
, Satish R. Devane2
, Shamim Akthar3
1
Department of Computer Engineering, Datta Meghe College of Engineering, Maharashtra, India
2
Department of Information Technology, Datta Meghe College of Engineering, Maharashtra, India
3
Department of Pathology, NKP SIMS RC & Lata Mangeshkar Hospital, NKP Salve Institute of Medical Science, Maharashtra, India
Article Info ABSTRACT
Article history:
Received April,2021
Revised Dec, 2021
Accepted Jan, 2022
In developing countries such as India, with a large aging population and
limited access to medical facilities, remote and timely diagnosis of myocardial
infarction (MI) has the potential to save the life of many. An
electrocardiogram is the primary clinical tool utilized in the onset or detection
of a previous MI incident. Artificial intelligence has made a great impact on
every area of research as well as in medical diagnosis. In medical diagnosis,
the hypothesis might be doctors' experience which would be used as input to
predict a disease that saves the life of mankind. It is been observed that a
properly cleaned and pruned dataset provides far better accuracy than an
unclean one with missing values. Selection of suitable techniques for data
cleaning alongside proper classification algorithms will cause the event of
prediction systems that give enhanced accuracy. In this proposal detection of
myocardial infarction using new parameters is proposed with increased
accuracy and efficiency of the existing model. Additional parameters are used
to predict MI with more accuracy. The proposed model is used to predict an
early diagnosis of MI with the help of expertise experiences and data gathered
from hospitals.
Keywords:
Decision tree
Ensemble algorithm
Multi-layer perceptron
Myocardial infarction
Naïve Bayes
Neural network
Support vector machine
This is an open access article under the CC BY-SA license.
Corresponding Author:
Nusrat Parveen
Department of Computer Engineering, Datta Meghe College of Engineering
Navi Mumbai-400708, Maharashtra, India
Email: np.cm.dmce@gmail.com
1. INTRODUCTION
The mortality rates of cancer and myocardial infarction (MI) are very high nowadays. MI is the
clinical term describing a heart attack due to a lack of oxygenated blood to heart tissue due to a clogged artery.
Patients who have survived an MI incident are at a greater risk of other heart-related health problems later in
their lifetime. Amongst all harmful sicknesses, coronary heart attacks are taken into consideration as the most
widely wide-spread. Medical practitioners’ behavior so many surveys on heart sicknesses and accumulate
records of coronary heart patients, their ailment development, and symptoms. Every year heart ailment reasons
tens of millions of deaths globally. Many techniques and tools were developed for coronary heart disease
prediction by using medical doctors. Researchers have made efforts to expand the automated diagnosis systems
in order that accurate diagnosis ought to take place. Among these, the automated machine the usage of data
mining and artificial intelligence (AI)-based totally approach is the recent one used in the automated prognosis.
The motivation of the work is the lack of data available freely and really difficult to access patient’s data from
hospitals. Large datasets are required to find out the model accurately. It's also important to predict early MI
to save lots of the lifetime of several.

Int J Inf & Commun Technol ISSN: 2252-8776 
Detection of myocardial infarction on recent (Indian) dataset using machine learning (Nusrat Parveen)
21
In this research, the actual datasets are collected from the hospitals. This dataset is not sufficient to
offer to the model. Providing limited information restricts the training of the model resulting in compromised
results in terms of overfitting. To overcome this problem a new path is taken by creating a synthetic dataset to
provide information in bulk to the model. For this, continuous discussions with expertise and rigorous study
are done and a range of various parameters are calculated for early MI, MI, and non-MI. The datasets available
on Kaggle are not recent and also it is not an Indian dataset. It is of utmost necessity to collect a recent dataset.
Around 2149 patients’ data is collected from three hospitals in pastoral areas of Nagpur. Machine learning
models learn very well if datasets are in bulk. Therefore, the idea of the synthetic dataset is proposed and
datasets are generated based upon the actual dataset. The accuracy of models is extremely high.
Figure 1 shows the myocardial infarction. An attack occurs when one among the heart's coronary
arteries is blocked suddenly or has extremely slow blood drift. The foremost common MI is due to the
bifurcation of the left arteria coronaria. The usual explanation for sudden blockage during an arteria coronaria
is the formation of a thrombus. The grume typically forms inside an arteria coronaria that already has been
narrowed by atherosclerosis, a condition during which fatty deposits (plaques) build up along the walls of blood
vessels [1]. Risk factors that can be controlled are high cholesterol, high bp, diabetes, weight, family history,
smoking, unhealthy diet, lack of physical activities, and metabolic syndrome.
Risk factors that cannot control are the age of men greater than 45 and in women, it is considered
greater than 55. If father or brother diagnosed attack before 55 years aged or mother or sister diagnosed before
65 years aged [2]. This case history results in MI. Another factor is understood as Preeclampsia. This condition
can develop during pregnancy. The 2 main signs of Preeclampsia are an increase in vital signs and excess
protein within the urine [3]. The main purpose of this research is to find MI in an early stage by using the above
risk factors which will save the life of mankind.
Figure 2 shows the diagrammatic representations of the research idea. Diagnosis relies upon many
various sorts of (accurate) data, from patient history to physical examination to lab data to past medical records
and radiographic findings. Each patients’ lifestyle, body system, and history are different. It is vital to notice
that if the first prediction is feasible then the death rate with MI will certainly lessen and the lifetime of mankind
will upgrade. Most vital thing is to think about those parameters of MI that are not included in early research
but are most vulnerable for MI in today’s life.
There is always a scope to exit from the prevailing approach and explore beyond the limit of other
findings. Therefore, there's a requirement for designing a model which can predict MI early supported the
parameters fed to the model. To reinforce the accuracy of the prognosis of MI for clinicians and clinical
scientists, in our system, the input is gathered from many doctors personally and therefore the patient’s data
through proper channel with history of MI and this data set is given to the predictive model which then verifies
and validates the proposed model. Early detection of MI will save the lifetime of mankind. This technique is
going to be helpful to the doctor’s assistant, nurses to require timely action if the doctor is not available within
the hospital [4].
Figure 1. Myocardial infarction Figure 2. Proposed system [4]

 ISSN: 2252-8776
Int J Inf & Commun Technol, Vol. 11, No. 1, April 2022: 20-31
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2. RESEARCH METHOD
Timely hospital reporting and diagnosis are critical within the myocardial infarct. The prehospital
delay could even be a significant explanation for increased morbidity and mortality within the myocardial
infarct. This study finds a scarcity of realization and poor transportation facilities due to the main contributors
to the delay within the management of myocardial infarction. Misjudgment of symptoms and transport delays
still contribute foremost to pre-hospital delays. Systems of ST-segment–elevation myocardial infarction
(STEMI) care will be got to concentrate on these variables to make an enormous impact on patient outcomes
in ST-elevation myocardial infarction [5]. Atypical lipids, smoking, high blood pressure, diabetes, stomach
obesity, psychosocial factors, eating fruits, vegetables, and alcohol, and regular physical activity account for
several of the danger of myocardial infarct worldwide in both sexes and within the smallest amount ages
altogether regions. This finding suggests that approaches to stop are often supported by similar principles
worldwide and have the potential to prevent most premature cases of myocardial infarction [6]. Cardiologists
Dr. Ashar Khan (DM) and Dr. Tamim Fazil (Medicine) and other experts have given tons of input during this
research. All aspects of MI were discussed with the expertise. Many inputs are provided by them. There's a
variable parameter that is liable for shown within Table 1. Firstly, MI features are excerpted from a rigorous
study of literature review. Supported the literature review a survey is conducted and 20 expertise opinions are
taken. This survey revealed the foremost important factors that ought to be considered during the research like
diabetics, history of patients, diet, and stress. Still smoking, eating habits, and stress are not ready to include
during this as they're vital features. The rationale is the unavailability of the info at the time of admission of
the patients. And missing values affect the performance of the model. And filling missing values with mean
and median is not suggested by expertise. Because the wrong values can cause misclassification of the model.
Table 1. Parameters list (literature review) [7]–[17]
Sr. No Parameters
1 Age
2 High frequency of diabetes
3 Cigarette smoking
4 Overweight
5 Lethargy
6 Family history of early heart disease
7 A previous heart disease (PHF)
8 Depression
9 The ketone body oxidation increases MI
10 Non-pulsatile pulmonary blood flow in Fontan circulation
11 The HF with preserved ejection fraction (HFpEF)
12 Maternal mortality and morbidities
13 Thyroid dysfunction
14 In heart failure (HF), cardiac energy metabolism is deranged
15 Hormone replacement therapy
16 Illicit drug
17 A history of preeclampsia
18 An autoimmune condition
19 CKD (chronic kidney disease)
20 Stress.
21 Diabetes
22 Deficiency in Vit-D3
23 High blood pressure
24 ECG
25 High Cholesterol
2.1. Parameters excerpted from survey
Input features and their values are shown in Table 2 are extracted from the survey which is conducted
during the research.
2.2. Statical analysis
This was an observational study conducted at two hospitals located in Nagpur (Kamptee). Data was
collected prospectively of patients admitted within the hospital and treated for MI from March 2018 till Dec
2020. The information of patients is collected from the hospitals personally and analysis is completed.
Employing a typical questionary, information was sought regarding the history of ischemic heart disease,
coronary risk factors, time of onset of pain, pain type, patient’s history, cholesterol, and blood pressure (BP).
All parameters are considered and discussed the vulnerability of the parameters expertly and included during
this research. As per the expertise, smoking and stress are the foremost important or responsible factors for MI.

23
Though they are not included within the research because the right information is not provided by the patients
or not known by the relatives who are admitting the patients to the hospital.
Data is gathered from the hospitals from the patients’ reports. Patients are evaluated with age, sex,
ECG changes, biomarkers (CK-MB, TROP-I), angiography (LAD, LCA, RCA) cholesterol, BP (systolic,
diastolic), chest paint type (acute, chronic), diabetics, chronic kidney disease (CKD), autoimmune condition
(AC), family history (FH), hormone replacement therapy (HRT), thyroid dysfunction (TD), acute kidney injury
(AKI). The evaluation is administered with the assistance of experts. Statistical analysis is completed using
google form and therefore the graph generated during the survey for extracting the MI parameters. Patients’
data are collected and transformed into the specified format.
Table 2. Parameters list (survey)
Sr. No Parameters Disintegrated_parameters and values
1 Age Numeric
2 Sex Male=1, Female=0
3 ECG
ECG Changes
Yes=1, No=0
4 Biomarkers
CK-MB, TROP-I Changes
Yes=1, No=0
5 Angiography
Left anterior descending (LAD), left coronary artery (LCA), right
coronary artery (RCA) in percentage (Converted into 0.0 to 1.0)
6 Cholesterol Numeric
7 Blood Pressure (Bp)
Systolic, Diastolic
Numeric Values
8 Chest pain type
Acute, Chronic
Acute=2, Chronic=1
9 Diabetic Yes=1, No=0
10 History:
Chronic kidney disease (CKD), autoimmune condition (AC),
previous heart failure (PHF), hormone replacement therapy
(Hor_Rep), thyroid dysfunction (Thy_Dys), acute kidney injury
(AKI).
Yes=1, No=0
11 MI Early MI=0, MI=1, non-MI=2
In this proposal, experiences and knowledge of experience are used. Victimization of data to answer
queries alongside the study of various algorithms like SVM, NB, DT, LR, KNN, Ensemble, and NN and expert
opinion is taken into account. Various data pre-processing techniques like data cleaning and pruning also the
normalization of knowledge are important steps to use before feeding input to the model. Various steps are
involved as:
- Bucketization
It is used to make buckets for sub-features by disintegrating the main features into sub-features.
- Normalization
Data are normalized converted into numeric with the help of experts.
- Data cleaning and pruning
Data cleaning and pruning technique are performed on the chosen data in order that a correctly cleaned and
pruned dataset provides far better precision than an unclean one with missing values. Data cleaning is the
method of making data for the model by eradicating or altering data that is improper, imperfect, disparate,
redundant, or inadequately formatted [18]–[20].
3. RESULTS AND DISCUSSION
In Figure 3 to Figure 21 graphs are created concerning each parameter vs the total number of patients
count. A total of 565 patient data is collected from two hospitals. Of these, 65 patients’ data have missing
values. Therefore, it's not included in the research. Out of 500 data, there were 147 patients with angina, 150
were non-MI and 303 were of MI. To form data balanced each 150 approx. is taken into account for the
research. Total 450 data is given to the model. Data analysis is carried out in Table 3.

 ISSN: 2252-8776
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Figure 3. Graph between age vs total patient count
Figure 4. Graph between gender vs total patient
count
Figure 5. Graph between ECG vs total patient count
Figure 6. Graph between Ckmb vs total patient
count
Figure 7. Graph between trop-i vs total patient
count

25
Figure 8. Graph between LAD vs total patient count Figure 9. Graph between LCA vs total patient count
Figure 10. Graph between RCA vs total patient count
Figure 11. Graph between systolic vs total patient count

 ISSN: 2252-8776
26
Figure 12. Graph between diastolic vs total patient count
Figure 13. Graph between chest pain vs total patient count
Figure 14. Graph between diabetic vs total patient count

27
Figure 15. Graph between cholesterol vs total patient count
Figure 16. Graph between CKD vs total
patient count
Figure 17. Graph between AC vs total
patient count
Figure 18. Graph between PHF vs total patient
count
Figure 19. Graph between Hor_Rep vs
total patient count

 ISSN: 2252-8776
28
Figure 20. Graph between Thy_Dys vs total
patient count
Figure 21. Graph between AKI vs total
patient count
Table 3. Description of graph
Parameters
MI=0 MI=1 MI=2
Total patient count=150 Total patient count=150 Total patient count=150
Age age>65 32% age>45 30% age>50 24%
Sex
Male=90% Male=95% Male=67%
Female=10% Female=5% Female=33%
ECG
99% Yes
100% yes
66% yes
1% No 34% No
Biomarkers
Ckmb=88% yes Ckmb=99% yes Ckmb=70% yes
Trop-I=88% yes Trop-I=99% yes Trop-I=70% yes
Angiography
LAD=60% patients having
90% blockage
LAD=35% patients having 100%
blockages
LAD=16% patients having
80% blockages
LCA=42% patients having
80% blockages
LCA=33% patients having 100%
blockages
LCA=7% patients having
90% blockages
RCA=59% patients having
90% blockages
RCA=28% patients having 100%
blockages
RCA=1% patients having
90% blockages
Cholesterol 45% patients having 180 38% patients having 180 22% patients having 190
Bp
Systolic 69% patients having
140
Systolic 50% patients having 110
Systolic 28% patients having
140
Diastolic 89% patients having
90
Diastolic 79% patients having 60
Diastolic 70% patients
having 90
Chest pain
Type
Chronic 92% Chronic 1% Chronic 30%
Acute 6% Acute 99% Acute 3%
No pain 2% No pain 0% No pain 66%
Diabetic 85% diabetic 6% diabetic 36% diabetic
History
CKD=99% No CKD=100% No CKD=100% No
AC=100% No AC=100% No Ac=100% No
PHF=76% yes PHF=99% No PHF=90% No
Hor_Rep=100% No Hor_Rep=100% No Hor_Rep=100% No
Thy_Dys=100% No Thy_Dys=100% No Thy_Dys=100% No
AKI=100% No AKI=100% No AKI=98% No
3.1. Experimental result
The dataset of two hospitals situated in Nagpur (Kamptee) is employed to classify three sorts of MI,
i.e. Early MI (angina), Non-MI, and MI. Various algorithms are applied to the present dataset which has 450
patients’ information. It is observed that the best results were achieved using MLP (alpha=0.7). Other’s
algorithms also are giving better accuracy within the training and testing phase. The output of algorithms can
be seen in Table 4. Though the result's appreciable, it is suggested further to add more patient details to see the
accuracy of the model. Because the data is especially from one region. It is going to vary from region to region
because the lifestyle, eating habits and stress levels change. Though these parameters are not included within
the research due to the unavailability of the knowledge. But expertise already emphasized this feature.
Therefore, it is suggested to consider more datasets on this to predict accurately. For this a novel idea is
proposed i.e., to generate synthetic datasets. The following steps are applied for the creation of a synthetic
dataset.

29
Table 4. Output of algorithms
Algorithms Training Set (%) Testing Set (%)
Linear SVM 93% 91%
RBF SVM 98% 83%
Gaussian process 95% 91%
Naïve Bayes (NB) 80% 82%
Decision tree (DT) 96% 91%
Random forest (RF) 94% 91%
K-nearest neighbors (KNN) 94% 91%
Neural network (NN) 94% 91%
AdaBoost 88% 85%
Quadratic discriminant analysis 33% 33%
MLP classifier (alpha=0.1) 95% 91%
3.1.1. Function for generation of synthetic datasets
For a generation of synthetic datasets, firstly histogram of every feature is generated i.e., distribution
of the information. Then normalized the histogram by scaling between zero and one. This distribution of data
is then passed to the function that's used to prepare the synthetic datasets.
here:
l is lower limit of data
u is the upper limit of data
n is the number of samples to be generated
d is the distribution based on actual dataset
3.1.2. Graph for synthetic dataset
The distribution of actual datasets is passed to the function to get synthetic datasets. And 45000 patient
report is generated from 2149 actual data gathered from patients' reports. The value of n is increased from 1k to
15k. 1k, 2k, 4k, 6k, 8k, 9k, 11k, 12k are giving NAN values. After 15k model accuracy is either constant or
reducing. Therefore, the creation of synthetic data is stopped at 45000 samples.
3.1.3. The result on synthetic datasets
Table 5 listed the accuracy of the models for 15000 samples of synthetic datasets at the training and
testing phase. In this KNN, RF is giving the highest accuracy.
Table 5. Algorithm accuracy at 15000 samples
Synthetic Datasets_15000
Algorithms Training Testing
K-nearest neighbors 99.91 99.96
Linear SVM 99.99 100
RBF SVM 100 80
Decision tree 100 100
Random forest 98.42 98.1
Neural network 99.99 100
AdaBoost 100 100
Naïve Bayes 99.26 99.45
Quadratic discriminant analysis (QDA) 99.26 99.43
4. CONCLUSION
This study has attempted to research the dataset about the input features and customary reasons for
early MI in patients presenting to the hospital within the urban area of Nagpur (Kamptee). There are previous
studies shown only about MI not included Early MI. There's lagging in data also that was not recent data. It's
also noticed that the Indian data is not available. This research has been done from scratch. Dataset is collected
from the two hospitals and expert assistance is taken to incorporate some important features for early MI. After
the gathering of knowledge from hospitals, the info is analyzed and it's discovered that in 450 patients there's
almost no change in AC, Hor_Repl, Thy_Dys, AKI parameters. It'd be this pathological test is not referred to

 ISSN: 2252-8776
30
during this area due to expensive or could be not responsible most for MI during this region. As per expertise
opinion, these parameters can be eliminated.
Feature selection is performed on 450 patients’ data. More data is collected for the creation of synthetic
datasets. 2149 patients’ info is collected, Data cleaning and pruning technique is applied. A distribution graph
is generated on this dataset and passed to the function to create synthetic datasets. This is done to create an
authentic dataset. Expertise opinion is also taken on each step. Further work can be carried out by considering
this opinion of experts. It is also suggested to collect more data from various regions of India to validate this
work.
ACKNOWLEDGEMENTS
I am thankful and acknowledge the full support from Dr. Asher Khan (Cardiologist), Dr. Tamim Fazil
(Medicine), Dr. Mehrosh Ghazal (Ped), Dr. Amera Ansari (Gyn), and Dr. Shamim Akhter (Path). I also thank
them for allowing me to collect data from the hospitals. I am also thankful to all 20 doctors who had responded
to my questionnaire through Google Form.
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BIOGRAPHIES OF AUTHORS
Nusrat Parveen is pursuing Ph.D. She has 19 years of teaching experience. She
is good in various subjects such as machine learning, web application and database. Nusrat’s
research is mainly focused on medical diagnosis using machine learning. She has published
8 papers in international conference, 3 international journals, 4 in national conferences and
one chapter is publish in book under Tailor & Francis (CRC-press). She won cash prize in
Indo-Korean festive competitions for outstanding innovator. She can be contacted at email:
np.cm.dmce@gmail.com.
Satish R. Devane is an Academician of the IIT (Ph.D: Information Technology
| M.E: Electronics | B.E.: Elcctronics |) and principal of KBTCOE, Nashik. Professor Devane
is proficient in many technical areas such as networking, artificial intelligence and data
minning. He has published 12 papers in international conferences. He can be contacted at
email: srdevane@yahoo.com.
Shamim Akhtar is MBBS, MD (pathology), gold medalist and IOSR-JDM
Global Editor. He has 30 years of experience. He published 17 papers in Int. journal. He has
also published 3 books on “Solved question paper of pathology & genetics for B Sc nursing”,
“Essential to genetics and pathology”, “Exam preparative manual for BDS students”.
Internationally invited as guest speaker and presenting recent research work at Montreal
international translational medicine conference-2011. Invited as guest speaker and for
presenting recent research work at Beijing international infectious diseases & antibiotics
conference in Beijing (China)-2011. Best teaching and academics awards received. He can
be contacted at email: akhtar_lmh@rediffmail.com

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