Big Data Analytics in Healthcare: Machine Learning-Based Cardiac Disease Prediction in West Africa

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.