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A Model to Detect Heart Disease using Machine Learning Algorithm

O.E. Taylor1 , P. S. Ezekiel2 , F.B. Deedam Okuchaba3

Section:Research Paper, Product Type: Journal Paper
Volume-7 , Issue-11 , Page no. 1-5, Nov-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i11.15

Online published on Nov 30, 2019

Copyright © O.E. Taylor, P. S. Ezekiel, F.B. Deedam Okuchaba . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: O.E. Taylor, P. S. Ezekiel, F.B. Deedam Okuchaba, “A Model to Detect Heart Disease using Machine Learning Algorithm,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.11, pp.1-5, 2019.

MLA Style Citation: O.E. Taylor, P. S. Ezekiel, F.B. Deedam Okuchaba "A Model to Detect Heart Disease using Machine Learning Algorithm." International Journal of Computer Sciences and Engineering 7.11 (2019): 1-5.

APA Style Citation: O.E. Taylor, P. S. Ezekiel, F.B. Deedam Okuchaba, (2019). A Model to Detect Heart Disease using Machine Learning Algorithm. International Journal of Computer Sciences and Engineering, 7(11), 1-5.

BibTex Style Citation:
@article{Taylor_2019,
author = {O.E. Taylor, P. S. Ezekiel, F.B. Deedam Okuchaba},
title = {A Model to Detect Heart Disease using Machine Learning Algorithm},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {11 2019},
volume = {7},
Issue = {11},
month = {11},
year = {2019},
issn = {2347-2693},
pages = {1-5},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=4937},
doi = {https://doi.org/10.26438/ijcse/v7i11.15}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i11.15}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=4937
TI - A Model to Detect Heart Disease using Machine Learning Algorithm
T2 - International Journal of Computer Sciences and Engineering
AU - O.E. Taylor, P. S. Ezekiel, F.B. Deedam Okuchaba
PY - 2019
DA - 2019/11/30
PB - IJCSE, Indore, INDIA
SP - 1-5
IS - 11
VL - 7
SN - 2347-2693
ER -

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Abstract

Heart disease also refers to conditions that involve narrowed or blocked blood vessels that can lead to a heart attack, chest pain (angina) or stroke. This paper presents a model for detecting heart disease using machine learning algorithm. The methodology adopted in this research is Agile Methodology, which follows planning, requirements analysis, designing, coding, testing and documentation in parallel during the stage of production process. In this paper a Heart Dataset was trained using four different machine learning algorithms (K-Nearest Neighbours Classifier, Support Vector Classifier, Decision Tree Classifier and Random Forest Classifier). The algorithm with the best accurate result was used in making predictions. This model was deployed to the web using flask (a python framework), it takes 13 inputs from the user in order to make prediction. The model is implemented using python programming language and flask (a web base framework). This paper uses a Decision Tree Classifier Algorithm and the results obtained from the prediction shows an accuracy of about 98.83%, which is really encouraging.

Key-Words / Index Term

Heart Disease, Machine Learning, K-Nearest Neighbors, Support Vector machine, Decision Tree, Random Forest

References

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