A Survey on Deep Learning Model for Improved Disease Prediction with Multi Medical Data Sets

Human society has great influence and threat from vibrant diseases as they get affected by different diseases every day. The cardiac disease prediction method predicts the disease based on the data set available and the features, whereas the performance of the method in predicting at higher accuracy is depend on the method and measures used. This article presents a detailed survey on predicting the cardiac disease with the support of deep learning Algorithms using varying data sets. The methods of disease prediction are classified according to the core approach and each class of disease prediction has been categorized based on the approach used. In general, the problem of disease prediction is approached with PCA (Principle Component Analysis), PSO (Particle Swarm Optimization), Genetic Algorithm, Fuzzy logic, neural network, and so on. This article is focusing on analyzing the deep learning method towards the prediction of different disease with various data sets. Further, the performance analysis is conducted and presented in detail. A comparative study has been presented for the better understanding of the methods and how they have been adapted. However, there are a lot of approaches available, the deep learning-based approaches produced higher performance in disease prediction.