An Intelligent Machine Learning Framework for Early Cardiovascular Disease Risk Prediction

Abstract

Heart disease continues to be a major health problem across the world, and identifying it at an early stage can help improve treatment and reduce serious complications. In recent years, machine learning has become a useful approach for studying patient health records and predicting the possibility of heart disease. This survey reviews and compares different machine learning techniques such as Logistic Regression, Decision Tree, Support Vector Machine, Random Forest, K-Nearest Neighbors, Gradient Boosting, and XGBoost. It also looks at common data preparation methods, including data cleaning, feature selection, handling imbalanced datasets using SMOTE, and model tuning to improve prediction results. From the studies reviewed, ensemble-based methods like XGBoost and Gradient Boosting generally provide more consistent and accurate predictions than many traditional algorithms. This also discusses the advantages, limitations, and research opportunities in this area. Overall, the survey shows that machine learning can support doctors by providing faster and more reliable predictions, making early diagnosis of heart disease more effective.