Survey of Heart Disease Prediction and Identification using Machine Learning Approaches

Heart disease is highlighted as the major one among the various death factors. Detecting heart disease tends to be a bit complex due to insufficient knowledge and experience of the medical practitioners concerning warning signs of heart failure. There exist innumerable data volumes in the healthcare sector. By adopting the best appropriate data mining techniques, early detection of heart-related diseases can be achieved and also preventing it from occurring. Both the Machine Learning (ML) and Data Mining (DM) techniques prove to be effective and significant in the domain of the medical industry. The objective of the current research work is to examine various risk parameters highlighted in the investigation of Heart disease and also it targets to discover multiple techniques for the identification and prediction of heart disease along with evaluating the drawbacks of the existing work. The article utilizes the DM techniques to summarize existing researches concerning heart disease prediction, examining a combination of DM techniques to reveal the best suitable and effective technique. The CNN, LSTM is being proposed for heart disease identification and prediction which yields in improvised output than the rest of the prevailing techniques. Various levels involved in the proposed approach are a collection of dataset, training, and testing, collection of user symptoms, securely forwarding the data by utilizing AES, and eventually, result is being generated in PDF format. Comparative performance in datasets concerning medicine is adopted in predicting heart disease techniques in comparison to the rest of other ML approaches.