Role of transfer functions in PSO to select diagnostic attributes for chronic disease prediction: An experimental study

Abstract

Particle Swarm Optimization (PSO) is a classic and popularly used meta-heuristic algorithm in many real-life optimization problems due to its less computational complexity and simplicity. The binary version of PSO, known as BPSO, is used to solve binary optimization problems, such as feature selection. Like other meta-heuristic optimization techniques designed on the continuous search space, PSO uses the transfer functions (TFs) to map the candidate solutions to the discrete search space in BPSO, and these TFs play a vital role to get the desired results. Over the years, many forms of TFs have been introduced in the literature, most of which fall under one of the five families - Linear, S-shaped, V-shaped, U-shaped, and Time-varying Mirrored S-shaped TFs. The goal of this study is to determine an appropriate setup constituting a TF and a classifier for feature selection from different types of clinical data. In this study, the impacts of the five TF families have been investigated, considering one from each family for the selection of attributes/features, while predicting disease using diagnosis or medical reports. The classification tasks are carried out using four standard classifiers: Support Vector Machine, Decision Tree, K-Nearest Neighbors, and Gaussian Naive Bayes. For experimental purposes, we have used four publicly available datasets namely, the UCI Heart Disease dataset, Wisconsin Breast Cancer dataset, UCI Chronic Kidney Disease dataset, and PIMA Indians Diabetes dataset. After an exhaustive set of experiments, we have obtained 96.72%, 99.82%, 100.00%, and 84.41% disease prediction scores in the best case for Heart disease, Breast Cancer, Chronic Kidney disease, and Diabetes, respectively. The obtained results are comparable to several state-of-the-art methods considered here for comparison. The present study helps in selecting a suitable BPSO setup (i.e., a TF and a classifier) to select important diagnostic attributes useful to design a computer-aided decision support system for the said diseases.