A Novel Ensembled Deep Machine Learning Framework for the Prediction of Chronic Disease

Abstract

The state of the environment and human behaviour today contribute to a wide range of diseases that affect people. To avoid such diseases reaching their worst, it is crucial to recognise and anticipate them in their early stages. Medical professionals sometimes find it challenging to appropriately detect disorders by manual procedures. The main intension of the proposed work is to detect and forecast patients having chronic diseases, such as chronic kidney disease, at their early stage. In this chapter, a novel ensemble model has been implemented by combining machine learning model with deep learning model. The combination of the ensembled machine learning and deep learning model is termed as ensembled deep machine learning framework (EDPMLF). The performance of the implemented EDPMLF have been validated using various parameters such as confusion matrix, Precision, AUC score, recall, and accuracy. The experimental investigation reveals that the proposed framework attained higher accuracy compared to the traditional approaches.