Prediction of Heart Disease by Developing the Hybrid Deep Learning Models to Attain Higher Accuracy

Abstract

One of the most common long-term diseases in the world is cardiovascular disease, also called heart disease. It is hard to make accurate and quick predictions about heart problems. Most of the work that has been done so far to identify heart disease has used machine learning methods, but they have not been able to get more accurate results. Recent advances in deep learning methods have a big effect on data analysis. Combining convolutional neural networks via a memory (LSTM), which network is what this work is all about. It aims to be additional exact than added ML methods. The heart disease information was put into two groups: normal and abnormal. This was done using a mixed CNN and LSTM method. The k-fold cross-validating method remained used to prove that this combination system works 90% of the time. Different machine learning algorithms, like SVM, Naïve Bayes, and Decision Tree, are compared with the suggested method to see how well it works. The outcomes show that future method works improved than the ML models that are already in use.