Hybrid Secure Encryption-Based Mathematical Model for Machine Learning-Driven Cardiac Disease Prediction

Diseases related to the heart are among the most common causes of death worldwide. Therefore, it is essential to have a correct, reliable, and secure predictive model to support timely clinical decision-making. This study presents a machine learning-based hybrid mathematical model along with encryption for predicting heart diseases. This framework includes robust data preprocessing, feature encryption, and collective learning to improve diagnostic accuracy while ensuring the confidentiality of patients’ data. The framework uses three main classifiers—Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), and Decision Tree (DT)—along with a voting-based collaborative approach that combines their complementary strengths. In this study, a dataset of ۱,۰۲۶ cases with ۱۴ medical parameters related to cardiac disease from a public dataset is used for the experimental setup. F۱-score, recall, precision, and accuracy are considered performance evaluation metrics. The proposed framework achieves ۹۸.۱۰% accuracy, compared with ۸۲.۱۰% for Decision Tree, ۹۰.۹۰% for Support Vector Machine (SVM), and ۹۴.۵۰% for XGBoost, along with ۹۸.۱۲% precision, ۹۸.۰۰% F۱-score, and ۹۶.۰۰% recall, demonstrating improved flexibility and generalization. The secure encryption layer further enhances the framework by protecting sensitive patient features while preserving statistical learning properties.

کلیدواژه ها:

Hybrid Machine learning ، Heart Disease Prediction ، Secure Data Encryption ، Ensemble model ، Medical Decision Support ، Mathematical model