IJMLC 2019 Vol.9(6): 762-767 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2019.9.6.870

A Machine Learning Approach: Using Predictive Analytics to Identify and Analyze High Risks Patients with Heart Disease

Fadoua Khennou, Charif Fahim, Habiba Chaoui, and Nour El Houda Chaoui

Abstract—The risk of developing early heart disease is always prominent. In fact, according to the Central of Disease Control and Prevention (CDC), Cardiovascular disease accounts for nearly 801,000 deaths in the US.
In this paper, we present a machine learning and decision based system for heart disease prediction.
We validate our approach on the Heart Disease Dataset gathered from the UCI machine learning repository.
Cleveland, Hungarian and Switzerland datasets are combined and trained with the use of SVM and Naïve Bayes algorithms.
Comparison and analysis with other models show that the accuracy of the proposed approach is 87% and 86% for SVM and Naïve Bayes respectively, which is much higher comparing to the existing approaches that use a small subset of data and no imputation technique.

Index Terms—Heart disease, machine learning, prediction, SVM, Naïve Bayes.

Fadoua Khennou and Nour El Houda Chaoui are with the Transmission and Treatment of Information Laboratory, Higher School of Technology, Sidi Mohamed Ben Abdellah University, Fez Morocco (e-mail: fadoua.khennou@usmba.ac.ma, houda.chaoui@usmba.ac.ma).
Charif Fahim is with the System Engineering Laboratory, ADSI Team, National School of Applied Sciences, Ibn Tofail University, Kenitra Morocco (e-mail: charif.fahim@gmail.com, mejhed90@gmail.com).

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Cite: Fadoua Khennou, Charif Fahim, Habiba Chaoui, and Nour El Houda Chaoui, "A Machine Learning Approach: Using Predictive Analytics to Identify and Analyze High Risks Patients with Heart Disease," International Journal of Machine Learning and Computing vol. 9, no. 6, pp. 762-767, 2019.

Copyright © 2019 by the authors. 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 (CC BY 4.0).

 

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Scopus (since 2017), Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net