Big data analytics in healthcare: machine learning-based cardiac disease prediction in West Africa
- Annales de l'Université Joseph KI-ZERBO , F (4) : 1-20
Résumé
This paper investigates the application of machine learning for cardiac disease prediction in resource constrained healthcare settings. This study conducts an empirical study evaluating four classification algorithms (Support Vector Machine, Random Forest, Logistic Regression, Decision Tree) on a real-world dataset. The results demonstrate that SVM achieves the highest accuracy (91%) in identifying high-risk patients, highlighting
its potential for clinical decision support. The study provides a detailed comparative analysis of model performance, discusses computational feasibility, and outlines practical deployment considerations. These findings contribute to the advancement of machine learning applications in African healthcare systems.
Mots-clés
Big Data Analytics, Data-driven healthcare, Data analytics in healthcare, Machine Learning in Healthcare, Disease Prediction