Parkinson Disease Detection Using XGBoost and SVM

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 09 Issue: 04 | Apr 2022 www.irjet.net p-ISSN: 2395-0072
© 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 708
Parkinson Disease Detection Using XGBoost and SVM
Parag P Chinawale1, Tanmay Maryapgol2, Atharv Rayar3, Jyoti Gaikwad4
1,2,3Student, Computer Engineering Department, Mumbai University, Datta Meghe College of Engineering, Navi
Mumbai, Maharashtra, India
4Professor, Computer Engineering Department, Mumbai University, Datta Meghe College of Engineering, Navi
Mumbai, Maharashtra, India
---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Parkinson's disease (PD), is the second most
common neurological disease that causes significant
disability, lowers the quality of life, and has no cure. The nerve
cells in this part of the brain are responsible for producing a
chemical called dopamine. Dopamine acts as a link between
parts of the brain and the nervous system that help control
and coordinate body movements. As dopamine is usually the
neurons in the cells begin to enter or complete another simple
task. About 90% of people with Parkinson's disease have an
early age of about 70 years, and the incidence increases
dramatically. However, a small percentage of people with PD
have a "first" disease that starts before the age of 50. More
than 10 million people worldwide live with PD. There is no
cure for PD today, but research is still ongoingandmedication
or surgery may offer significant improvements in motor
symptoms.
Key Words: Parkinson disease, Artificial Intelligence,
Machine Learning
1.INTRODUCTION
Parkinson's disease (PD) is a chronic, debilitating
neurological disorder. ThemaincauseofParkinson'sdisease
is unknown. However, it hasbeenstudiedthata combination
of natural and genetic factors plays a key role in causing PD
[1]. To put it bluntly, Parkinson's disease is treated as a
disorder of the nervous system thatresultsinthelossofcells
in various parts of the brain. These cells include the
substantia nigra cells that produce dopamine. Dopamine
plays an important role in movement planning. []It acts as a
chemical messenger to transmit signals within the brain. As
a result of the loss of these cells, patients have difficulty
moving.
In many cases, doctorsfindit difficulttoassumethat
a given patient has already been diagnosed or is expected to
have Parkinson's disease [4]. To overcome this, the
development of a specific computer model should be
performed by examining and summarizing the givenpatient
data and predicting sufficient accuracy whenundergoing PD
development. Most PD patients are treated with symptoms
called voice defects are known as dysphonia. There are
several steps associated with dysphonia, in which a voice-
related problem can be used to diagnose patients at various
stages [14]. This paper is a PD prediction survey using
machine learning and in-depth Artificial Intelligence
strategies that produce good models and strengths of those
algorithms in terms of acquired accuracy,andintermsofthe
various methods used.
2. LITERATURE SURVEY:
2.1 IMPORTANCE OF VOICE DATA:
Speech or voice data is considered 90% useful. To diagnosea
person to detect the presence of a disease. In general, a PD
person suffers from speech problems, which is they can be
divided into two: hypophonia and dysarthria. Hypophonia
indicates a soft, weak voice from the person and dysarthria
shows a slow speech or voice, which can never understood
simultaneously and this results in damage in the central
nervous system. Thus, most therapists PD patients
experience dysarthria and try to recover from it certain
medications to improve the tone of voice.
3. METHODOLOGY
Upon pre-processingandloadingthedatasetthefirstattempt
is to perform Exploratory Data Analysis to understand the
data available and identify the important featuresformaking
accurate models. [3]This is done using Feature Importance
analysis, a class of techniques for assigning scores to to find
the important featuresfromthegiveninputdata,eachfeature
that is used while making a prediction. Feature importance
analysis provides insight into the dataset. [2]The relative
scores highlight which features is morerelevanttothetarget.
The most important scores are calculated by a predictive
model that has been fit on the dataset. After preparing the
data and gaining valuable insight about important features
from the data is implementing various modeling techniques.
The modeling techniques are implemented to test the
accuracy of models with the training as well as test data. The
various modeling techniquesproposedforuseareasfollows-
3.1 SUPPORT VECTOR MACHINE:
Support Vector Machine is a new generation oflearninga
program based on the latest advances in mathematical
learning vision. It is your algorithm for both linear and
indirect data. [3]Converts original data to higher magnitude,
from where it can find the top flight of data segmentation
using important training tuples called support vectors. The

SupportVectorMachineisadiscriminatorycategoryofficially
defined by separating the hyper plane. [5]In another names,
given labeled training Vector vector support builds a high-
rise aircraft or a set of high-altitude planes at high altitudes
or an infinite-dimensional space, which can be used
separation, retreat, or other activities. Intuitively, well the
separation is achieved by a hyper plane with very large
distance to the nearest training datacenterofanyclasscalled
a functional margin, as it is usually underminestheseparator
generalization error The useful created data is then applied
on the pre loaded SVM algorithm.
Fig 1: SVM Code
3.2 XGBOOST:
XGBoost is another use of gradient boosting (GB) algorithm,
based on the tree of determinationas divides into categories.
Used for its speed, efficiency, and proportions. By type, GB
and XGBoost can be explained. [3]If we have D = [x, y] it
represents data sets that contain n observation, i.e. x feature
(independent variables) and y dependent variations. In GB,
imagine that there is a value of k development, rather than
having the B function of predicting the effect using Ÿ as a
predictor of i-th sample b-th boost, means the formation of a
tree q, which has a j-shaped leaf. [1]Then for a given sample,
the final prediction can be determined by summarizing the
points further all leaves, this is shown in Eq.1
Fig -2: Formula for XGBoost
Fig -3: Code for XGBoost
4. ANALYSIS:
The front end of the system is created using stremlit and the
interface is kept simple for better understanding. The front
end consists of various voice and speech related parameters
which user needs to enter. The parameters are then
processed at the backend and the output is displayed on the
frontend. The accuracy of the system is 94.87%.
Fig 4: Frontend Output
Fig 5: Accuracy Level

5. CONCLUSION
The paper is an effort for providing the broader view of
Parkinson Disease and the methods such as Machine
Learning and Artificial Intelligence use to detect it. In the
Methodology in above section the algorithms usedprovidea
good amount of accuracy using the voice and speech
parameters.[4] By the previous research it has been
identifies that the new machine learning and artificial
intelligence technologies can maximize the output if
combined together.
REFERENCES
[1] Implementation of Xgboost for classification of
parkonsion’s disease: G Abdurrahman, M Sintawati –
2020.
[2] Detection of Parkinson's Disease by Employing
Boosting Algorithms - Mirza MuntasirNishat,Tasnimul
Hasan , Sarker Mohammad Nasrullah , Fahim Faisal –
2021.
[3] Vocal Feature Extraction-Based Artificial Intelligent
Model for Parkinson’s Disease Detection – Muntasir
Hoq , Mohammed Nazim Uddin , Seung-Bo Park – 2021.
[4] Akshaya Dinesh and Jennifer He, "Using Machine
Learning to Diagnose Parkinson's Disease from Voice
Recording", in Proceedings of the IEEE MIT
Undergraduate Research Technology Conference
(URTC), pp. 1-4, 2017.
[5] Ipsita Bhattacharya et al, SVM Classification to
Distinguish Parkinson Disease Patients, Conference:
Proceedings of the 1st Amrita ACM-W Celebration on
Women in Computing in India, September 16-17, 2010

Parkinson Disease Detection Using XGBoost and SVM

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