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Related Experiment Video

Updated: May 21, 2025

An Application for Pairing with Wearable Devices to Monitor Personal Health Status
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Evaluating and implementing machine learning models for personalised mobile health app recommendations.

Hafsat Morenigbade1, Tareq Al Jaber2, Neil Gordon2

  • 1Centre of Excellence for Data Science, AI and Modelling, Faculty of Science and Engineering, University of Hull, United Kingdom.

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Summary
This summary is machine-generated.

This study developed a healthcare app recommendation system using machine learning. The BERT model achieved ~90% accuracy, improving mobile health (mHealth) app discovery for users.

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Area of Science:

  • Digital Health
  • Health Informatics
  • Machine Learning in Healthcare

Background:

  • The proliferation of mobile health (mHealth) applications necessitates effective evaluation and recommendation systems.
  • A growing mHealth market underscores the need for tools to guide users to relevant health applications.

Purpose of the Study:

  • To design and evaluate a recommendation system for mHealth applications.
  • To leverage app attributes, particularly descriptions, for contextual user suggestions.
  • To categorize health applications for improved user experience.

Main Methods:

  • A dataset of health app attributes, including descriptions, ratings, and reviews, was curated.
  • Data pre-processing involved one-hot encoding, standardization, and feature engineering, including a novel 'Rating_Reviews' feature.
  • Machine learning and deep learning models, including Random Forest and BERT, were evaluated using 'Category' (e.g., 'Weight loss', 'Medical') as the target variable.

Main Results:

  • The BERT model, utilizing transfer learning, demonstrated high efficiency, achieving approximately 90% accuracy after hyperparameter tuning.
  • Feature engineering, including the 'Rating_Reviews' metric, contributed to model performance.
  • The 'Category' variable effectively distinguished different health contexts within the mHealth landscape.

Conclusions:

  • Transfer learning, particularly with the BERT model, is highly effective for mHealth application recommendation.
  • A robust recommendation system can be built using app descriptions and user-centric features.
  • The developed system, employing cosine similarity, accurately ranks apps based on user query relevance.