An Integrative Framework for Healthcare Recommendation Systems: Leveraging the Linear Discriminant Wolf-Convolutional Neural Network (LDW-CNN) Model
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View abstract on 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.
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.
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