Cardiovascular Disease Classification Using Advanced Machine Learning Techniques: A Comparative Study

Abstract

Cardiovascular disease is yet a leading cause of death and requires accurate diagnostic tools. This research presents a machine learning-based approach using algorithms like KNN, SVM, Decision Tree, Random Forest, and Gradient Boosting for disease classification. The dataset was preprocessed by tackling the missing values problem and feature standardization to obtain critical predictors using feature selection methods. These algorithms were assessed by the following metrics such as precision, recall, F1-score, and accuracy.  After pre-processing the dataset, Gradient Boosting achieved the highest accuracy (92%), followed by Random Forest (89%). Key predictors like cholesterol, blood pressure, and age were identified. The study shows that ensemble methods have potential in medical datasets but identifies the challenges of data imbalance and limited generalizability and encourages future work with deep learning and larger datasets to improve early diagnosis and patient outcomes.