Prediction of Heart Disease and Heart Failure Using Ensemble Machine Learning Models

Heart disease, commonly referred to as cardiovascular disease and heart failure, has been the leading cause of mortality globally. Many risk factors for heart disease are associated with prompt access to reliable, dependable, and practical early diagnosis and disease management procedures. Identifying heart disease through early-stage signs is challenging in today’s global climate. If not caught in time, this could result in death. When there are no heart specialist doctors in remote, semi-urban, or rural areas, precise risk prediction and analysis might be critical in the early-stage identification of heart disorders. Machine learning (ML) and Deep learning (DL) approaches were employed in this study to assess massive volumes of complex medical data, supporting specialists in predicting heart illness and mortality from heart failure. This study used two datasets: one to forecast heart disease and the other to analyze and forecast death due to heart failure. Predicting cardiac illnesses using Artificial Neural Networks is 91.52% accurate (ANN). The bagging ensemble predicted heart failure with 90% accuracy. The primary contribution of this research is an ensemble strategy with high performance that multiple measurements have demonstrated to predict heart failure and cardiac disorders using ANN.