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Updated: Sep 28, 2025

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Deep Learning in mHealth for Cardiovascular Disease, Diabetes, and Cancer: Systematic Review.

Andreas Triantafyllidis1, Haridimos Kondylakis2, Dimitrios Katehakis2

  • 1Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece.

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Summary

Deep learning (DL) applied to mobile health (mHealth) data shows promise for diagnosing and managing chronic diseases like cardiovascular disease, diabetes, and cancer. This review highlights DL

Keywords:
chronic diseasedeep learningmHealthmobile phonereview

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence
  • Digital Health

Background:

  • Major chronic diseases (cardiovascular disease, diabetes, cancer) pose global health burdens.
  • Deep learning (DL) offers potential for intelligent mobile health (mHealth) interventions.
  • mHealth interventions can revolutionize healthcare delivery for chronic conditions.

Purpose of the Study:

  • To systematically review studies using DL with mHealth data for chronic disease management.
  • To advance understanding of DL applications in diagnosis, prognosis, and treatment.
  • To synthesize progress in this rapidly developing field.

Main Methods:

  • Systematic search of Scopus and PubMed for DL studies using mobile device data.
  • Focus on cardiovascular disease (CVD), diabetes, and cancer.
  • Synthesis of studies based on disease, participant demographics, DL algorithms, outcomes, and performance.

Main Results:

  • 20 studies reviewed: 35% CVD, 45% diabetes, 20% cancer.
  • DL applications included diagnosis (CVD, cancer) and blood glucose prediction (diabetes).
  • Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) were commonly used; DL achieved >84% accuracy, outperforming traditional machine learning.

Conclusions:

  • DL harnessing mHealth data can aid in chronic disease diagnosis, management, and treatment.
  • Further prospective studies are needed to validate DL in real-world mHealth tools.
  • Explainability of DL outcomes requires further investigation.