This paper focuses on the evaluation and recommendation of healthcare applications in the mHealth field. The increase in the use of health applications, supported by an expanding mHealth market, highlights the importance of this research. In this study, a data set including app descriptions, ratings, reviews, and other relevant attributes from various health app platforms was selected. The main goal was to design a recommendation system that leverages app attributes, especially descriptions, to provide users with relevant contextual suggestions. A comprehensive pre-processing regime was carried out, including one-hot encoding, standardisation, and feature engineering. The feature, “Rating_Reviews”, was introduced to capture the cumulative influence of ratings and reviews.
The variable ‘Category’ was chosen as a target to discern different health contexts such as ‘Weight loss’ and ‘Medical’. Various machine learning and deep learning models were evaluated, from the baseline Random Forest Classifier to the sophisticated BERT model.
The results highlighted the efficiency of transfer learning, especially BERT, which achieved an accuracy of approximately 90% after hyperparameter tuning. A final recommendation system was designed, which uses cosine similarity to rank apps based on their relevance to user queries.
Citation
Morenigbade, H., Al Jaber, T., Gordon, N., & Eke, G. (2025). Evaluating and implementing machine learning models for personalised mobile health app recommendations. PLoS ONE, 20(3), e0319828. https://doi.org/10.1371/journal.pone.0319828
