Cardiovascular diseases are a major global health concern, requiring early risk identification and effective preventive healthcare strategies. Traditional assessment methods rely on clinical measurements, medical history, and expert interpretation, which may limit the identification of complex relationships among patient attributes. This research proposes a Personalized Healthcare Analytics Framework for Early Cardiovascular Risk Prediction Using Explainable Machine Learning. The framework applies data preprocessing techniques, including missing-value handling, feature encoding, normalization, and exploratory data analysis, to improve data quality. Feature selection methods identify relevant clinical attributes for model development. Multiple machine learning algorithms, including Logistic Regression, Random Forest, Support Vector Machine, and XGBoost, are trained and evaluated using appropriate performance metrics. An ensemble learning approach combines complementary model predictions to improve predictive performance. Probability calibration assesses the reliability of estimated risk scores, while SHAP-based explanations identify influential clinical features at both global and individual patient levels. Model performance is evaluated using accuracy, precision, recall, F1-score, ROC-AUC, and calibration metrics. The proposed framework aims to support interpretable cardiovascular risk assessment and assist healthcare professionals in making informed preventive decisions.

 

Keywords: Cardiovascular Risk Prediction, Explainable Machine Learning, Ensemble Learning, Feature Selection, SHAP Explainability, Personalized Healthcare.