Moloud Abdar, Vivi Nur Wijayaningrum, Sadiq Hussain, Roohallah Alizadehsani, Pawel Plawiak, U. Rajendra Acharya, Vladimir Makarenkov
Wart 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. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.
Département d’Informatique, Université du Québec à Montréal, Montréal, QC, Canada; Department of Information Technology, Politeknik Negeri Malang, Malang, East Java, Indonesia; Dibrugarh University, Dibrugarh, Assam, India; Institute for Intelligent Systems Research and Innovations (IISRI), Deakin University, Geelong, VIC, Australia; Institute of Telecomputing, Faculty of Physics, Mathematics and Computer Science, Cracow University of Technology, Krakow, Poland; Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore; Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore, Singapore; Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia