Enhancing Early Diagnosis and Identification of Cardiovascular Diseases using Machine Learning

With the advent of advanced digital technologies revolutionizing the healthcare sector, there is a significant focus on accurate machine learning models to predict chronic heart diseases. The late detection and misdiagnosis of chronic diseases like cardiovascular disease can significantly increase the mortality rate. Therefore, a reliable, precise, and effective system must be in place to find these illnesses in time for appropriate treatment. Large-scale and sophisticated data processing has been automated using machine learning techniques and algorithms for various medical data repository. Several scholars have recently developed machine-learning techniques to assist the medical community and experts in detecting heart-related illnesses. The approach assists in predicting chronic diseases like heart failure and effective rehabilitation and timely management. This paper surveys various machine-learning models and compares their accuracy and other parameters in diagnosing heart-related disease. In this paper, we aim to compare various machine-learning models; a predictive model based on the stacking of different classifiers are suggested for the prediction of heart disease. This model suggests fostering accurate decision-making. This proposed model will enhance prediction accuracy and eliminate anomalies, thus justifying the selection of the stacking classifier as the most accurate machine-learning model to predict heart failure by about 98%.