Cardiovascular Disease Prediction Using Machine Learning and Deep Learning Algorithms

Heart is the human body's most vital organ. As a result, heart disease is considered as a serious condition, which may carry a significant long-term risk of death. However, the existing approaches cannot efficiently identify inherent correlations and patterns from e-health data. To save lives, a detailed data analysis process is required. Incorporating such analysis is difficult but it is highly essential to implement disease detection. This study suggested using machine learning to perform health analytics, which would allow for more accurate patient data analysis. Here, data mining techniques can be used to develop an intelligent model based on patient risk factor datasets. This study analyses how deep learning and machine learning approaches can be used to diagnose heart diseases. Recently, various data mining classifiers are developed to accurately and effectively identify heart diseases. MLP, a complex classification algorithm, employs artificial neural networks with deep learning capabilities. To generate accurate findings, the proposed method makes use of deep learning and data mining techniques.