APPROACHES TO PREDICT CARDIOVASCULAR ISSUE USING MACHINE LEARNING METHOD

Abstract

Cardiovascular illnesses remain a major health concern, requiring efficient detection techniques. Despite valuable research, gaps remain in predictive models, particularly due to imbalanced datasets, leading to biased predictions. This study employs machine learning to detect cardiac issues, including myocardial infarction, addressing dataset imbalance. It evaluates Fuzzy C-Means Clustering, Quadratic Discriminant Analysis (QDA), Support Vector Machine (SVM), Multi-layer Perceptron (MLP), and Stochastic Gradient Descent (SGD). The findings offer insights into enhancing myocardial infarction prediction and improving cardiovascular disorder diagnosis.