Advancing Predictive Analytics for Cardiovascular Disease using Machine Learning and Deep Learning

This study explores the application of machine learning and deep learning techniques in forecasting heart disease, a pressing concern given the increasing prevalence of cardiovascular conditions worldwide. The timely and precise diagnosis of potential heart disease cases is crucial in increasing the chances of survival, and this work finds resolution by adopting the use of modern computational tools. Several aspects of data pre-processing are used in the research to enable completeness of data and normalization of data set originating from various sources such as demographic data, medical history, and diagnostic tests. This work uses logistic regression, decision trees, ensemble methods, CNNs, and ANNs to recognize patterns of potential risks of heart diseases. The results show enhanced performance of these ML and DL models that are superior to the conventional diagnosis techniques. In particular, the ML models have an AUC bigger than 0. 85, which is a sign of a good predictive model. Moreover, the DL models, especially the CNNs and ANNs yielded even better outcomes than them with AUC ratings higher than 0. 90, proving their capacity to identify non-linear dependencies in the data better than linear regression models. Based on the findings of this research, it becomes clear that the application of ML and DL in the field of health care is revolutionary; especially in diagnosing and treating heart diseases earlier and more effectively. Through the application of such advanced algorithms into clinical decisionmaking, healthcare providers can improve their ability to accurately predict patients with heart diseases, which will subsequently translate into better patient care and improved global health on the same. In this case, the study supports the increased use of technologies in order towards realizing enhanced advancements in cardiovascular diseases.