An Intelligent Machine Learning Framework for Early Cardiovascular Disease Risk Prediction

Abstract: Heart disease is one of the leading causes of death worldwide, making early prediction essential for improving patient outcomes. Machine learning techniques have gained significant attention for predicting heart disease by analyzing medical and clinical data. This survey paper reviews recent studies on heart disease prediction using various machine learning algorithms, including Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, K-Nearest Neighbors, Gradient Boosting, and XGBoost. It also discusses feature selection techniques, data preprocessing methods such as SMOTE, and model optimization approaches that improve prediction performance. Based on the reviewed literature, ensemble learning methods, particularly XGBoost and Gradient Boosting, demonstrate superior accuracy and reliability. This survey highlights the strengths, limitations, and future scope of machine learning-based heart disease prediction systems and emphasizes their potential to support early diagnosis and clinical decision-making.

Keywords: Heart Disease Prediction, Machine Learning, Cardiovascular Disease, Classification Algorithms, Explainable Artificial Intelligence (XAI), Feature Selection, SMOTE, Healthcare Analytics.