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 Khennou1
,
Charif Fahim2 , ,
Nour El Houda Chaoui1
- 1Transmission and Treatment of Information Laboratory, Higher School of Technology, Sidi Mohamed Ben Abdellah University, Fez Morocco
- 2System Engineering Laboratory, ADSI Team, National School of Applied Sciences, Ibn Tofail University, Kenitra Morocco
- Manuscript receivedAugust 2, 2019
- revisedOctober 11, 2019
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.
Keywords
- Heart disease
- machine learning
- prediction
- SVM
- Naïve Bayes
How to 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, vol. 9, no. 6, pp. 762-767, 2026. https://doi.org/10.18178/ijmlc.2019.9.6.870
Copyright & License
Copyright © 2026 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).