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

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 andK10). Our database consisted of 180 records taken from immunotherapy and cryotherapy databases. The best results were obtained using theK10 protocol that provided the precision, recall, F-measure and accuracy values of0.8908, 0.8943, 0.8916 and90%, respectively. Our IAPSO system showed the reliability of98.68%. 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.
Source: Journal of Medical Systems - Category: Information Technology Source Type: research