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IAPSO-AIRS: a novel improved machine learning-based system for wart disease treatment
journal contribution
posted on 2019-07-01, 00:00 authored by Moloud Abdar, V N Wijayaningrum, S Hussain, Roohallah Alizadehsani, P Plawiak, U R Acharya, V MakarenkovWart disease (WD) is a skin illness on the human body which is caused by the human papillomavirus (HPV). This study mainly concentrates on common and plantar warts. There are various treatment methods for this disease, including the popular immunotherapy and cryotherapy methods. Manual evaluation of the WD treatment response is challenging. Furthermore, traditional machine learning methods are not robust enough in WD classification as they cannot deal effectively with small number of attributes. This study proposes a new evolutionary-based computer-aided diagnosis (CAD) system using machine learning to classify the WD treatment response. The main architecture of our CAD system is based on the combination of improved adaptive particle swarm optimization (IAPSO) algorithm and artificial immune recognition system (AIRS). The cross-validation protocol was applied to test our machine learning-based classification system, including five different partition protocols (K2, K3, K4, K5 and K10). Our database consisted of 180 records taken from immunotherapy and cryotherapy databases. The best results were obtained using the K10 protocol that provided the precision, recall, F-measure and accuracy values of 0.8908, 0.8943, 0.8916 and 90%, respectively. Our IAPSO system showed the reliability of 98.68%. It was implemented in Java, while integrated development environment (IDE) was implemented using NetBeans. Our encouraging results suggest that the proposed IAPSO-AIRS system can be employed for the WD management in clinical environment.
History
Journal
Journal of medical systemsVolume
43Article number
220Pagination
1 - 23Publisher
SpringerLocation
New York, N.Y.Publisher DOI
ISSN
0148-5598eISSN
1573-689XLanguage
engPublication classification
C1 Refereed article in a scholarly journalCopyright notice
2019, Springer Science+Business Media, LLC, part of Springer NatureUsage metrics
Keywords
Science & TechnologyLife Sciences & BiomedicineHealth Care Sciences & ServicesMedical InformaticsWart diseaseData miningMachine learningComputer-aided diagnosis systemArtificial immune recognition systemImproved adaptive particle swarm optimizationPARTICLE SWARM OPTIMIZATIONCLASSIFICATIONALGORITHMDIAGNOSISULTRASOUNDPREDICTIONSEGMENTATIONVALIDATIONBREASTCOLONYInformation Systems
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