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A Smart Healthcare Recommendation System for Multidisciplinary Diabetes Patients with Data Fusion Based on Deep

Baha Ihnaini1, M A Khan2, Tahir Abbas Khan3

  • 1Department of Computer Science, College of Science and Technology, Wenzhou-Kean University, Wenzhou 325060, China.

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Accurate diabetes prediction is crucial for timely treatment. This study introduces a smart healthcare system using deep learning and data fusion, achieving 99.6% accuracy for early diabetic disease detection and recommendation.

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Machine Learning for Disease Prediction

Background:

  • Diabetes mellitus is a global, life-threatening, multidisciplinary disease affecting vital organs.
  • Existing machine learning models struggle with large, multi-feature diabetes datasets.
  • Accurate prediction and timely treatment of diabetes remain significant challenges.

Purpose of the Study:

  • To develop a smart healthcare recommendation system for accurate diabetes prediction and management.
  • To enhance prediction accuracy by integrating deep machine learning with data fusion techniques.
  • To improve computational efficiency and system performance in handling complex healthcare data.

Main Methods:

  • Implementation of a novel smart healthcare recommendation system for diabetes.
  • Application of data fusion techniques to optimize feature selection and reduce computational load.
  • Training and evaluation of an ensemble machine learning model for disease prediction.
  • Comparison with state-of-the-art deep machine learning methods on a standard diabetes dataset.

Main Results:

  • The proposed system achieved a high accuracy of 99.6% in diabetes prediction.
  • The data fusion approach enhanced system performance and computational efficiency.
  • The ensemble model demonstrated superior predictive capabilities compared to existing methods.
  • The system effectively handles large, multi-feature healthcare datasets.

Conclusions:

  • The developed smart healthcare recommendation system offers superior performance for multidisciplinary diabetes prediction.
  • The integration of deep learning and data fusion significantly improves diagnostic accuracy.
  • The system's high accuracy supports its potential use in automated diagnostic and recommendation systems for diabetic patients.