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October 9, 2025Biomedical Physics & Engineering ExpressOpen Access

Cardiovascular Risk Prediction in Diabetes: A Hybrid Machine Learning Approach

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Authors

IRImran RehanMRMujeeb Ur Rehman

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Overview

Hybrid machine learning framework improves cardiovascular risk prediction in diabetes, indicating potential for clinical use.

Key Points

  • The hybrid model achieved an impressive accuracy of 98.7%, providing reliable cardiovascular risk predictions for diabetic patients.
  • Incorporating both LSTM and traditional algorithms significantly boosted cardiovascular disease prediction accuracy compared to conventional methods.
  • Model performance remained consistent across diverse demographic groups, suggesting equitable health outcomes across populations.
  • The framework's strong predictive ability indicates potential for clinical deployment to improve patient outcomes and enhance health equity.

Cite This Study

Rehan et al. (2025) studied this question.

synapsesocial.com/papers/68e70da790569dd607ee5a21https://doi.org/10.1088/2057-1976/ae103a
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Also Consider

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  1. 1A Diabetes Prediction Model Using Hybrid Machine Learning Algorithm2024 · 3 citations
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  3. 3Diabetes Prediction using Hybrid Model2024
  4. 4Supervised Machine Learning Approach for Predicting Cardiovascular Complications Risk in Patients with Diabetes Mellitus2024
  5. 5Evaluation of cardiovascular disease in diabetic patients using machine learning techniques2024