Chronic disease prediction with deep convolution based modified extreme-random forest classifier

A disease is said to be chronic when a disease occurs in an individual and their health conditions due to the disease last one year/more. Predicting chronic diseases have become crucial to save individual’s life and enhance their well-being. Though traditional clinical procedures are considered to perform this, it seems to be a time consuming process. Concurrently, with the progress of data mining algorithms, researchers have attempted to use different algorithms for such prediction. Nevertheless, they have been ineffective in feature extraction that negatively affected the prediction rate. To combat issues with regard to low accuracy rate, the present research intends to perform prediction of four common chronic diseases (breast cancer, heart disease, diabetes and kidney disease) affecting people worldwide. To accomplish this, the research proposes dual Deep CNN (Deep Convolutional Neural Network) for feature extraction. In this case, optimal, maximum and minimum hidden layers are used for extracting relevant features. Further, ME-RF (Modified Extreme-Random Forest) is used for classification. In this process, the research considers XGBoost algorithm comprising of certain innate advantages like high convergence and modest computations. However, when the predictability of this model is poor, it works in an ideal manner with numerous leaves in DT (Decision Tree). Simultaneously, RF comprise of several trees with equal weighted leaves by which, maximum precision and accuracy could be attained flexibly with the prevailing data. Considering this, the trees are built with RF and the research introduces this process as ME-RF. Classification performance is evaluated individually on four different considered datasets under the implementation of minimum and maximum Deep CNN network and also with the use of combined dual deep CNN networks. The overall analytical outcomes confirms the effectiveness of the proposed system.