ISSN (Online) 2581-9429

IJARSCT
International Journal of Advanced Research in Science, Communication and Technology (IJARSCT)

Volume 2, Issue 3, November 2022

Impact Factor: 6.252

Heart Disease Prediction using ML

Prajakta Bhosale1, Sakshi Mulik2, Apoorva Shirke3, Tanmay Pathare4 ,Priyanka Jagtap5
Students, Department of Information Technology1,2,3,4
Teacher, Department of Information Technology5,
Sinhgad Institute of Technology, Lonavala, Maharashtra, India

Abstract: As per the recent study by WHO, heart related diseases are increasing. 17.9 million people die
every-year due to this. With growing population, it gets further difficult to diagnose and start treatment at
early stage. But due to the recent advancement in technology, Machine Learning techniques have
accelerated the health sector by multiple researches. Thus, the objective of this paper is to build a ML
model for heart disease prediction based on the related parameters.

Keywords: K-Nearest Neighbour Algorithm (KNN), Logistic regression algorithm (LR), and Naive
Bayes(NB), Linear support vector machine

I. INTRODUCTION
It is difficult to identify heart disease because of several contributory risk factors such as diabetes, high blood pressure,
high cholesterol, abnormal pulse rate and many other factors. Among various life threatening diseases, heart disease has
garnered a great deal of attention in medical research. The diagnosis of heart disease is a challenging task, which can
offer automated prediction about the heart condition of patient so that further treatment can be made effective. The
diagnosis of heart disease is usually based on signs, symptoms of the patient. The severity of the disease is classified
based on various methods like K-Nearest Neighbour Algorithm (KNN), Logistic regression algorithm (LR), and Naive
Bayes(NB),Linear support vector machine. The nature of heart disease is complex and hence, the disease must be
handled carefully. Not doing so may affect the heart or cause premature death.

II. PROPOSED WORK

The proposed work predicts heart disease by exploring the above mentioned four algorithms and dose performance
analysis. The objective of this study is to effectively predict if the patient suffers from heart disease. The health
professional enters the input values from the patient’s health report. The data is fed into model which predicts the
probability of having heart diseases.

III. UML DIAGRAM

3.1 Activity Diagram

Copyright to IJARSCT DOI: 10.48175/568 4

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 ISSN (Online) 2581-9429
IJARSCT
International Journal of Advanced Research in Science,, Communication and Technology (IJARSCT)

Volume 2, Issue 3, November 2022

Impact Factor: 6.252
3.2 Class Diagram

3.3 Block Diagram

IV. HARDWARE AND SOFTWARE

SOFTWAR REQUIREMENTS
4.1 Hardware
 Windows and redhat linux minimum 4gb ram i5 processor

4.2 Software
 Jupyter notebook

4.3 Languages
 Python (backend)
 Html,css(frontend)

V. APPLICATIONS
5.1 Medical Institutes
To teach medical students how the heart attack been measured, or how to identify that the person is suffering from heart
disease.

5.2 Hospitals
To detect that is the person having heart disease or not.

VI. FUTURE SCOPE

 In future we can be made to produce an impact in the accuracy of the Decision Tree and Bayesian
Classification for additional improvement after applying genetic.

Copyright to IJARSCT DOI: 10.48175/568 5

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 ISSN (Online) 2581-9429
IJARSCT
International Journal of Advanced Research in Science, Communication and Technology (IJARSCT)

Volume 2, Issue 3, November 2022

Impact Factor: 6.252
 Algorithm in order to decrease the actual data for acquiring the optimal subset of attribute that is enough for
heart disease prediction. The automation of heart disease prediction using actual real time data from health
care organizations and agencies which can be built using big data. They can be fed as a streaming data and
 By using the data, investigation of the patients in real time can be prepared.

VII. CONCLUSION
Identifying the processing of raw healthcare data of heart information will help in the long term saving of human lives
and early detection of abnormalities in heart conditions. Machine learning techniques were used in this work to process
raw data and provide a new and novel discernment towards heart disease.Heart disease prediction is challenging and
very important in medical field. However, the mortality rate can be drastically controlled if the disease is detected at
early stage and preventive measures are adopted as soon as possible. The proposed System is combined the
characteristics of K-Nearest Neighbour Algorithm (KNN), Logistic regression algorithm (LR), and Naive
Bayes(NB),Linear support vector machine. Proposed System proved to be quite accurate in the prediction of heart
disease.

ACKNOWLEDGEMENT
The authors would like to thank Professor of the Department of Information Technology in Sinhgad institute of
technology, Lonavala Prof. P. T.Jagtap for their time and efforts She provided throughout the project and the guidance,
advice and suggestions by her were really helpful to us.

REFERENCES
[1]. An_Effective_Heart_Disease_Detection_and_Severity_Level_Classification_Model_Using_Machine_Learnin
g_and_Hyperparameter_Optimization_Methods.pdf
[2]. A_Stacking-Based_Model_for_Non-Invasive_Detection_of_Coronary_Heart_Disease.pdf
[3]. https://www.academia.edu/52218642/Heart_Disease_Prediction_Using_Machine_Learning_Algorithms
[4]. https://vemanait.edu.in/pdf/cse/18-19-Paper/Mrs.Jayashree-L%20K-Heart-Disease-Protection-System.pdf
[5]. 10.1109@ICE348803.2020.9122958.pdf
[6]. https://ieeexplore.ieee.org/abstract/document/9491140
[7]. https://ieeexplore.ieee.org/abstract/document/9831786
[8]. https://ieeexplore.ieee.org/document/9751602
[9]. https://ieeexplore.ieee.org/abstract/document/9358597

BIOGRAPHY
 Prajakta P. Bhosale - An Undergraduate Scholar pursuing Bachelors of Engineering in Information
Technology from Sinhgad Institute of Technology. She is working under the guidance of Prof. P. T.Jagtap
 Sakshi A.Mulik - An Undergraduate Scholar pursuing Bachelors of Engineering in Information Technology
from Sinhgad Institute of Technology. She is working under the guidance of Prof. P. T.Jagtap
 Apoorva P.Shirke - An Undergraduate Scholar pursuing Bachelors of Engineering in Information Technology
from Sinhgad Institute of Technology. She is working under the guidance of Prof. P. T.Jagtap
 Tanmay M. Pathare - An Undergraduate Scholar pursuing Bachelors of Engineering in Information
Technology from Sinhgad Institute of Technology. He is working under the guidance of Prof. P. T.Jagtap

Copyright to IJARSCT DOI: 10.48175/568 6

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