A Model to Detect Heart Disease using Machine Learning Algorithm

Authors

  • OE Taylor Dept. of Computer Science, Rivers State University, Port Harcourt, Nigeria
  • PS Ezekiel Dept. of Computer Science, Rivers State University, Port Harcourt, Nigeria
  • FB Deedam-Okuchaba Dept. of Computer Science, Rivers State University, Port Harcourt, Nigeria

DOI:

https://doi.org/10.26438/ijcse/v7i11.15

Keywords:

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

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.

References

[1]. K. Vanisree, S. Jyothi, “Decision Support System for Congenital Heart Disease Diagnosis based on Signs and Symptoms using Neural Networks”, International Journal of Computer Applications vol.19, issue.6, pp.6 – 12, 2011.

[2]. S.F. Weng, J. Reps, J. Kai, J.M. Garibaldi, N. Qureshi, “Can Machine-Learning Improve Cardiovascular Risk Prediction Using Routine Clinical Data”, vol.1, issue.12, pp. e0174944, 2017.

[3]. M. Thiyagaraj, G. Suseendran, “Survey on heart disease prediction system based on data mining techniques”, Indian Journal of Innovations and Developments vol.6 issue.1, pp.1-9, 2017.

[4]. C.S. Dangare, S.S. Apte, “Improved Study of Heart Disease Prediction System using Data Mining Classification Techniques”, International Journal of Computer Applications vol.47, issue.10, pp. 44-48, 2012.

[5]. S. Palaniappan, R. Awang, “Intelligent heart disease prediction system using data mining techniques”, In 2008 IEEE/ACS international conference on computer systems and applications, pp. 108-115, 2008.

[6]. C.B.C. Latha, S.C. Jeeva, “Improving the accuracy of prediction of heart disease risk based on ensemble classification techniques”, Informatics in Medicine, Unlocked 16, pp.100203, 2019.

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Published

2019-11-30
CITATION
DOI: 10.26438/ijcse/v7i11.15
Published: 2019-11-30

How to Cite

[1]
T. OE, E. PS, and D.-O. FB, “A Model to Detect Heart Disease using Machine Learning Algorithm”, Int. J. Comp. Sci. Eng., vol. 7, no. 11, pp. 1–5, Nov. 2019.

Issue

Vol. 7 No. 11 (2019): IJCSE November Edition

Section

Research Article

License

Creative Commons License

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