Ensemble Deep Learning-Powered AI for Early Cardiovascular Disease Detection and Prediction

Medical professionals require accurate risk assessment strategies for disease prevention through early interventions since cardiovascular conditions continue to be the globe’s principal cause of deaths and disabilities. By integrating sophisticated deep learning systems researchers have achieved improved predictive modeling through comprehensive medical dataset analysis. This research explores a thorough assessment of present-day deep learning solutions used for cardiovascular risk evaluation. The paper starts with an assessment of traditional risk assessment methods while highlighting their deficiencies before moving towards sophisticated model development. The text examines deep learning fundamentals as well as its successful ability to process elaborate medical information with multiple dimensions. Medical specialists shortages and the increasing number of incorrect diagnoses call for an efficient precise detection system of heart disease. The current medical data mining mixed with machine learning techniques assist in heart attack predictions yet they do not achieve consistent accuracy. A complex system is presented in this research to predict heart attacks by integrating RFXGB (Random Forest eXtreme Gradient Boosting) and YOLO (You Only Look Once). RFXGB combines Random Forest algorithm with XGBoost to perform both feature selection and classification tasks while YOLO functions as a program that analyzes medical images in real-time to enhance cardiovascular risk evaluation precision. Classifiers like Decision Tree and SVM models to distinguish between YES and NO, in which the proposed classifier achieved best accuracy of 96% when compared with the existing classifiers.