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Identifying Key Quality Features of mHealth Applications: Unsupervised Feature Selection Approach: MARS Case Study

Abstract:

In this work, the influential features to improve mHealth apps quality are identified, using a wrapper structure that integrates multi-objective evolutionary algorithm (MOEA) as a search method and clustering algorithm and index clustering performance as evaluation criteria. The study uses a dataset that comes from the employment of the mobile app rating scale (MARS) model. The dataset comprehends 565 Spanish language apps for people with special skills, published during the last twenty years. The results show that it is feasible to find specific characteristics that could lead to improving the apps, and the process used is a practical contribution directed to the stakeholders that promote the enhancement of the quality of life through the mHealth approach.