Early Prediction of Cardiac Diseases Using Artificial Intelligence, Mattibanana Extract, Molecular Docking, and Novel Drug Formulation

Abstract

Cardiovascular diseases (CVDs), including myocardial infarction and angina pectoris, are major causes of global mortality and morbidity. Delayed diagnosis, unhealthy lifestyle, stress, diabetes, hypertension, and hyperlipidemia significantly increase cardiac risk. Early prediction and effective treatment are essential for reducing complications and improving patient survival. This article proposes an integrated approach combining Artificial Intelligence (AI), phytopharmaceutical research, molecular docking, and novel drug formulation using Mattibanana plant extract as a natural cardioprotective agent. The AI-based predictive system analyzes clinical and physiological parameters such as ECG signals, blood pressure, cholesterol levels, glucose levels, heart rate variability, age, family history, obesity, smoking habits, and stress biomarkers. Machine learning and deep learning techniques including Random Forest, Support Vector Machine (SVM), Artificial Neural Networks (ANN), and Convolutional Neural Networks (CNN) are used to identify hidden patterns and predict the risk of myocardial infarction and angina at an early stage. The system aims to provide rapid, accurate, and low-cost cardiac risk assessment. Simultaneously, phytochemical constituents present in Mattibanana extract are screened for cardioprotective activity. Molecular docking studies are performed against cardiovascular targets such as angiotensin-converting enzyme (ACE), HMG-CoA reductase, and inflammatory proteins to evaluate binding affinity and therapeutic potential. Selected compounds are further analyzed using ADMET prediction to determine drug-likeness and safety profiles. Lead compounds with favorable properties are proposed for development into novel formulations such as tablets, nanoparticles, or nanoemulsions. This multidisciplinary approach provides a promising strategy for preventive cardiology and plant-based drug discovery.