Healthcare & Predictive Analytics
Machine learning framework for predicting susceptibility to obesity
This paper introduces ObeRisk, a novel machine learning framework designed for accurate prediction of obesity susceptibility. It features a unique Entropy-Controlled Quantum Bat Algorithm (EC-QBA) for superior feature selection and an ensemble of machine learning models for robust prediction. ObeRisk achieves a remarkable 97.13% accuracy, significantly outperforming existing methods by integrating advanced AI techniques for proactive health management.
Executive Impact & Key Performance Indicators
ObeRisk's advanced AI capabilities translate directly into tangible improvements for healthcare organizations, offering unprecedented accuracy and efficiency in identifying at-risk individuals.
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The Challenge of Obesity Prediction
Obesity is a major global health crisis, increasingly linked to serious diseases like cardiovascular disease, type 2 diabetes, and certain cancers. Its prevalence has surged, making accurate and early identification of risk a critical challenge. Traditional methods often fall short in capturing the complexity of genetic, behavioral, nutritional, and environmental factors influencing obesity.
EC-QBA: A Novel Approach to Feature Selection
The core of ObeRisk's superior performance lies in its novel Entropy-Controlled Quantum Bat Algorithm (EC-QBA). This advanced feature selection method enhances the traditional Bat Algorithm by integrating Shannon entropy to dynamically control parameters and quantum-inspired mechanisms for local search, leading to improved solution diversity and avoidance of local optima. This ensures the most informative features are selected for prediction.
Ethical Implications of AI-Driven Risk Labeling
Labeling individuals at risk of obesity must be done transparently and empathetically to avoid social stigma and discrimination. ObeRisk prioritizes a probabilistic approach, not deterministic, and advocates for continuous bias examination in AI models. Healthcare providers are encouraged to communicate risk information with sensitivity and involve patients in decision-making processes.
Ensuring Trust with Model Interpretability
Understanding the 'why' behind AI predictions is crucial for trust and adoption in healthcare. ObeRisk employs SHapley Additive exPlanations (SHAP) values to explain feature importance, confirming that factors like 'weight' are primary indicators. This enhances physicians' comprehension of obesity risk factors and supports tailored interventions.
97.13%
Peak Predictive Accuracy Achieved
ObeRisk demonstrates exceptional performance in identifying obesity susceptibility, setting a new benchmark for accuracy in AI-driven health predictions.
ObeRisk Framework: End-to-End Prediction
Data Preprocessing (PS)
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Entropy-Controlled Quantum Bat Algorithm (FS)
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Ensemble Machine Learning & Majority Voting (OPR)
The ObeRisk framework streamlines the process from raw data to actionable obesity risk predictions through three interconnected stages.
| Model | Accuracy (%) | Precision (%) | Sensitivity (%) | F-measure (%) |
|---|---|---|---|---|
| ObeRisk (Proposed) | 97.13 | 95.7 | 95.5 | 95.6 |
| ML (Reference 19) | 95.4 | 93.5 | 93.0 | 93.25 |
| CIM (Reference 20) | 94.0 | 93.5 | 93.0 | 93.25 |
| CDSS (Reference 21) | 93.6 | 93.0 | 92.8 | 92.89 |
| DeepHealthNet (Reference 23) | 93.2 | 92.4 | 92.6 | 92.5 |
| ML-XAI (Reference 26) | 93.0 | 92.2 | 92.5 | 92.35 |
| Feature | ObeRisk (AI Framework) | BMI (Traditional) |
|---|---|---|
| Used Dataset | Lifestyle, age, activity, comprehensive health factors | Height and Weight only |
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Transforming Obesity Management with ObeRisk
ObeRisk represents a significant leap forward in AI-driven healthcare, offering a robust solution for a critical public health challenge. By integrating advanced feature selection with an ensemble of ML models, it provides highly accurate and interpretable predictions of obesity susceptibility.
This framework's ability to identify individuals at greatest risk empowers healthcare providers to implement timely and personalized interventions, leading to improved patient outcomes and more efficient resource utilization.
Future work will focus on further enhancing computational efficiency and generalizability across diverse populations, ensuring ObeRisk remains at the forefront of predictive analytics in health.
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