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An Integrative Framework for Healthcare Recommendation Systems: Leveraging the Linear Discriminant Wolf-Convolutional

Vedna Sharma1, Surender Singh Samant1, Tej Singh2

  • 1Department of Computer Science, Graphic Era (Deemed to be University), Dehradun 248002, India.

Diagnostics (Basel, Switzerland)
|November 27, 2024
PubMed
Summary
This summary is machine-generated.

A new LDW-CNN model improves healthcare recommendations by addressing class imbalance. This novel approach enhances disease prediction accuracy and diagnostic capabilities for better patient care.

Keywords:
deep learninggrey wolf optimizationhealth recordshealthcarelinear discriminant analysis

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Area of Science:

  • Artificial Intelligence in Healthcare
  • Machine Learning for Medical Diagnosis

Background:

  • Recommender systems are vital in healthcare for predicting patient and professional data.
  • Class imbalance is a significant challenge in healthcare recommendation systems.
  • Accurate prediction and diagnosis require high-quality, reliable, and authenticated information.

Purpose of the Study:

  • To address class imbalance in healthcare recommendation systems.
  • To enhance prediction and diagnostic accuracy using a novel LDW-CNN model.
  • To improve the performance of healthcare recommender systems.

Main Methods:

  • Integration of linear discriminant wolf (LDW) with convolutional neural networks (CNNs) to form the LDW-CNN model.
  • Utilizing grey wolf optimizer with linear discriminant analysis for enhanced prediction.
  • Evaluation on multi-disease datasets (heart, liver, kidney) and comparison with CNNs and multi-level support vector machines (MSVMs).

Main Results:

  • The LDW-CNN model achieved 98.1% accuracy, surpassing existing deep learning methods.
  • Improved specificity (99.18%) and sensitivity (99.008%) compared to conventional techniques.
  • Demonstrated superior predictive performance over traditional CNN and MSVM approaches.

Conclusions:

  • The LDW-CNN model offers a robust solution for multidisciplinary disease prediction and recommendation.
  • It provides superior performance in healthcare recommender systems.
  • The model enhances prediction and diagnosis across multiple disease domains due to high accuracy, specificity, and sensitivity.