Heart Disease Prediction: Integrating Machine Learning and Digital Twin Framework

This research introduces a new framework based on machine learning and digital twin in the transformation of heart disease prediction in healthcare. It presents novel solutions for predicting disease progression and stimulating all possible outcomes in advance of the treatment, real-time monitoring of patients, these ML models provide accurate predictions of disease trajectories and optimal strategies of treatment. Digital twins, virtual representation of patient’s data allow testing different treatment scenarios and therefore findings without the risk of the patients. This fusion of advanced technologies gives healthcare providers access to unprecedented insights, enabling them to give more effective, personalized, and patient-centric care. Such studies would be addressing critical challenges in healthcare with better patient impact and the optimal use of resources while contributing to greater advances in medical research.