ML-based Meta-Model Usability Evaluation of Mobile Medical Apps

Abstract

Mobile medical applications (MMAPPs) are one of the recent trends in mobile trading applications (Apps).MMAPPs permit users to resolve health issues easily and effectively in their place. However, the primary issue is effective usability for users in maps. Barely any examination breaks down usability issues subject to the user's age, orientation, trading accessories, or experience. The motivation behind this study is to decide the level of usability issues, concerning traits and experience of versatile clinical clients. The review utilizes a quantitative technique and performs client try and hypothetical insight through the survey by 677 members with six distinct assignments on the application's point of interaction. The posttry review is finished with concerning members. The Response surface method (RSM) is used for perceptional and experimental designs. In each case, participants are divided into 13 runs or groups. Experimental groups are involved after checking the perceptions about theoretical usability for different attributes according to the usability model through the questionnaire. The difference is recorded between the perception of users about usability (theoretical usability) and actual performance for usability. The study analyzed through Analysis of variance (ANOVA) that there is a need to improve mobile medical applications but it is also recommended to minimize the gap between the perception level of laymen and the actual performance of IT literate users in context with usability. The experimentation measures the tasks usability of various mobile medical applications concerning their effectiveness, efficiency, completeness, learnability, memorability, easiness, complexity, number of errors and satisfaction. Every design model also produces a mathematical expression to calculate usability with its attributes. The results of this study will help to improve the usability of MMAPPs for users in their convenient context.