Heart Disease prediction using MLP and LSTM models
Mohamed Djerioui, Youcef Brik, Mohamed Ladjal, Bilal Attallah
Abstract
One of the key causes of premature disability and mortality in the world today is heart disease, which makes its prediction a vital problem in the field of healthcare systems. This work provides a contribution to the study and creation an intelligent system based on LSTM technique for heart disease prediction. A comparative study is presented between Multi Layer Perceptron (MLP) and Long Short Term Memory (LSTM) techniques in terms of accuracy and other predictive parameters for heart disease. The main aim is to develop an intelligent system based on LSTM technique for predicting heart disease in order to make an adapted decision to prevent and monitor heart disease and stroke. As it has better characteristics than those of the MLP technique, LSTM is shown to be the most effective technique for solving the aforementioned problems.