IAPSO-AIRS: A novel improved machine learning-based system for wart disease treatment

• Published in: Journal of medical systems Vol. 43; no. 7; pp. 220 - 23
• Main Authors: Abdar, Moloud, Wijayaningrum, Vivi Nur, Hussain, Sadiq, Alizadehsani, Roohallah, Plawiak, Pawel, Acharya, U. Rajendra, Makarenkov, Vladimir
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.07.2019; Springer Nature B.V
• Subjects: Adaptive algorithms; Adaptive systems; Adolescent; Adult; Aged; Artificial intelligence; Classification; Cryotherapy; Data Mining; Diagnosis, Computer-Assisted; Environmental management; Female; Health Informatics; Health Sciences; Human papillomavirus; Humans; Immunotherapy; Learning algorithms; Machine Learning; Male; Medical diagnosis; Medical treatment; Medicine; Medicine & Public Health; Middle Aged; Particle swarm optimization; Protocol (computers); Reproducibility of Results; Skin diseases; Statistics for Life Sciences; Systems-Level Quality Improvement; Treatment Outcome; Wart disease; Warts; Warts - therapy; Young Adult; Artificial immune recognition system; Computer-aided diagnosis system; Wart disease; Data mining; Machine learning; Improved adaptive particle swarm optimization
• ISSN: 0148-5598; 1573-689X; 1573-689X
• DOI: 10.1007/s10916-019-1343-0

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 ( K 2, K 3, K 4, K 5 and K 10). Our database consisted of 180 records taken from immunotherapy and cryotherapy databases. The best results were obtained using the K 10 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.