An effective mechanism for early chronic illness detection using multilayer convolution deep learning predictive modelling

The study aims to predict the chronic disease of different patients using a multilayer convolution deep learning approach, which is a method of deep learning model that treats the input medical data as a vector representation. Additionally, the benefit of multi-layer perceptron is a class of neural networks in a feed-forward way which comprises mainly three layers of processing nodes for the detection of chronic diseases. The proposed system performed an efficient prediction for the diseases based on the mechanism which detects the patient can have a high rate of chronic conditions based on chronic illness. The proposed system accurately predicted that the patients are having a high rate of depressions, fatigue, joint pains, heart diseases, and strokes as chronic illnesses based on the past data applied to the system for the evaluations and analysis. The result shows that the higher accuracy and precision rate for the prediction of several diseases and at the same time low classification error rate using the proposed deep learning model. The proposed article is utilized the chronic illness dataset which consists of depression, fatigue, headache, various body pains symptoms to validate a practical methodology for predicting and handling chronic diseases with partly experimental information.