A Hybrid Deep Learning Algorithm Based Prediction Model for Sustainable Healthcare System

In the present era, maintaining a healthy and diseasefree life is complex due to multiple personal and environmental impacts. Early identification and diagnosis will help human beings lead a sustainable life. However, to achieve this, healthcare data have to be processed in an efficient manner with more accuracy. Thus, the impacts of diseases or future impacts can be predicted or detected and proper medication can be provided by the physicians. Handling medical data over conventional data analysis is quite different due to data diversity. Efficient feature extraction techniques must be employed with minimum computation cost so that the extracted features can be classified in a better way. Machine learning models perform well in healthcare data analysis. However, the performance can be improved if deep learning models replace machine learning models. Thus, in this research work, a hybrid deep learning approach is proposed using convolutional neural networks (CNN) and the random forest (RF) algorithm. The final classifier block in the CNN architecture is replaced with a RF classifier to enhance the prediction accuracy of 0.975 and overall performance. Standard benchmark healthcare datasets are employed in the proposed model simulation analysis and the performances are compared to existing techniques such as a MNN (multi-neural network), CNN-multilayer perceptron (CNN-MLP), CNN-long short-term memory (CNN-LSTM), support vector machines (SVM), and KNN to validate the superior performance.