Univrsity Of Tehran Press Journal of Information Technology Management 2980-7972 18 4 2026 10 01 Hybrid Secure Encryption-Based Mathematical Model for Machine Learning-Driven Cardiac Disease Prediction 59 69 108915 10.22059/jitm.2026.108915 EN Priya Gupta Research Scholar, Department of Computer Science and Engineering, Sanskriti University - Mathura-281401, India. Sakuldeep Singh Associate Professor, Department of Computer Science and Engineering, Sanskriti University- Mathura-281401, India. Journal Article 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 1,026 cases with 14 medical parameters related to cardiac disease from a public dataset is used for the experimental setup. F1-score, recall, precision, and accuracy are considered performance evaluation metrics. The proposed framework achieves 98.10% accuracy, compared with 82.10% for Decision Tree, 90.90% for Support Vector Machine (SVM), and 94.50% for XGBoost, along with 98.12% precision, 98.00% F1-score, and 96.00% recall, demonstrating improved flexibility and generalization. The secure encryption layer further enhances the framework by protecting sensitive patient features while preserving statistical learning properties. https://jitm.ut.ac.ir/article_108915_8c01eaa44e3e785dc3fcba07c879aa6a.pdf