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dc.contributor.authorMalakar, Samir
dc.contributor.authorSen, Swaraj
dc.contributor.authorRomanov, Sergei
dc.contributor.authorKaplun, Dmitrii
dc.contributor.authorSarkar, Ram
dc.date.accessioned2023-11-14T08:06:15Z
dc.date.available2023-11-14T08:06:15Z
dc.date.issued2023-09-22
dc.description.abstractParticle Swarm Optimization (PSO) is a classic and popularly used meta-heuristic algorithm in many reallife 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 Timevarying 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.en_US
dc.identifier.citationMalakar S, Sen, Romanov, Kaplun, Sarkar. Role of transfer functions in PSO to select diagnostic attributes for chronic disease prediction: An experimental study. Journal of King Saud University - Computer and Information Sciences. 2023;35(9)en_US
dc.identifier.cristinIDFRIDAID 2185169
dc.identifier.doi10.1016/j.jksuci.2023.101757
dc.identifier.issn1319-1578
dc.identifier.issn2213-1248
dc.identifier.urihttps://hdl.handle.net/10037/31744
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.relation.journalJournal of King Saud University - Computer and Information Sciences
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2023 The Author(s)en_US
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0en_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)en_US
dc.titleRole of transfer functions in PSO to select diagnostic attributes for chronic disease prediction: An experimental studyen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
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