SmartHeart AI: An intelligent machine learning system for early cardiovascular disease detection

Cardiovascular diseases (CVDs) are also considered as some of the most common causes of death in the world and are usually diagnosed up to the late stages, thus early diagnosis is much important in preventing those diseases. The current paper introduces the SmartHeart AI, an intelligent multi-modal machine learning model that will support early cardiovascular disease prediction based on structured clinical and lifestyle data, and be extended to include unstructured medical data and physiological observations. I propose a framework that uses information processing, feature engineering, and the use of ensemble learning approaches in modeling complex interrelationship between risk factors. An approach using the Random Forest approach has a better result, recording a high accuracy of about 85% and a ROC-AUC value of 0.90 which represents a great ability to predict. Probabilistic risk estimation is also provided to present patient-specific risk scores which improves its applicability in clinical decision support. There is also multi-modal data fusion design to provide future capability of integrating clinical text, wearable sensor and imaging modalities. The model has been experimentally tested to be robust and generalize well and the ability to interpret the features is supported by feature importance analysis. The suggested system brings out the opportunities of smart, data-driven solutions in enhancing early detection and preventative health services of cardiovascular diseases.