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Health Prediction Using Hybrid Deep Learning Models: A Transparent and Interpretable Approach.

Garima Verma1, Seema Yadav1

  • 1School of Computing, DIT University, Dehradun, India.

The International Journal of Health Planning and Management
|April 8, 2026
PubMed
Summary
This summary is machine-generated.

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This study introduces two hybrid Artificial Intelligence (AI) models for accurate obesity classification, outperforming traditional methods by integrating deep learning with advanced feature selection for better patient stratification.

Area of Science:

  • Medical Informatics
  • Artificial Intelligence
  • Public Health

Background:

  • Obesity is a global health crisis linked to chronic diseases, but traditional classification methods like Body Mass Index (BMI) are insufficient.
  • The multifactorial nature of obesity, involving genetics, lifestyle, and environment, complicates accurate categorization and treatment.
  • Existing methods struggle to capture complex biological and lifestyle variables, leading to misclassification and ineffective treatments.

Purpose of the Study:

  • To develop advanced Artificial Intelligence (AI) models for precise obesity classification.
  • To overcome the limitations of traditional BMI-based methods in capturing obesity's complexity.
  • To enhance patient stratification for more effective obesity management.

Main Methods:

Keywords:
SHAPdeep learningexplainable AIintegrated gradientsobesity classificationself‐attention LSTMtabnet

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  • Proposed two hybrid AI models: CNN + TabNet and Self-Attention BiLSTM (SA-BiLSTM) + TabNet.
  • Employed deep characterization learning combined with TabNet's sparse attention mechanism for feature selection.
  • Utilized feature engineering, class-balancing, and Explainable AI (XAI) tools (SHAP, LIME, IG) for model interpretability and robustness.

Main Results:

  • CNN + TabNet achieved 92% accuracy (0.925 recall, 0.917 F1-score).
  • SA-BiLSTM + TabNet achieved 94.1% accuracy (0.93 recall, 0.937 F1-score).
  • Both models demonstrated superior performance compared to conventional baselines.

Conclusions:

  • The hybrid models offer a novel approach to joint spatial-temporal learning for interpretable and data-efficient obesity prediction.
  • Blending deep characterizations with TabNet's feature selection enhances obesity classification accuracy and clinical utility.
  • This AI-driven approach represents a significant advancement over previous hybrid methods and standalone TabNet applications.