Enhanced Hybrid Feature Selection with HRFBP Algorithm for Medical Data Classification

Abstract

Medical industry produces a significant portion of data whereas by adopting various machine learning models which can make accurate predictions about public healthcare that can be generalised. Machine learning is ideal for examining complex high dimensional data because of its flexibility and automation compared to conventional approaches. Still, feature extraction is not performed effectively and hence overall classifier accuracy is reduced considerably. To overcome the above mentioned problems, in this work, Hybrid Random Forest with Back Propagation (HRFBP) neural network algorithm is proposed. Initially, the datasets are collected which is preprocessed using K-Means Clustering (KMC) algorithm. It is used to handle the missing values and error rates efficiently. Then, the feature extraction is done by using Modified Principal Component Analysis (MPCA) which is focused to extract the significant features from the given medical dataset. After that, the feature selection is done by using EFS algorithm which generates best fitness values via objective function. EFS is done dependent on integrating numerous FS rather than a single FS to handle the FS issue. The possibility of EFS method is that merging the outcomes of a various single FS methods like Entropy Elephant Herding Optimization (EEHO), Adaptive Firefly Optimization Algorithm (AFOA) and Entropy Butterfly Optimization Algorithm (EBFO) acquire improved outcomes rather than utilizing a single FS methodology. Finally, the medical dataset classification is performed using HRFBP algorithm. The HRFBP algorithm performs training and testing process which learns a set of weights for prediction for the class label of features. HRFBP increases the classifier accuracy and reduces the error rates prominently.  From the experimental result, it concluded that the proposed HRFBP algorithm provides better performance in terms of the higher accuracy, sensitivity, specificity and lower execution time rather than the existing algorithms