Hybrid Quantum-Classical-Generative AI Framework to Predict the Risk of Early Osteoporosis: A Comparative Study of Eight Intelligent Models
Abstract
Osteoporosis is a severe health issue to the world with millions of people being affected, with timely diagnosis being the most critical aspect in intervention and prevention of fragile bone fracture which has a serious effect on the quality of life of patients. The study provides a broad comparative study of eight different intelligent models across classical machine learning, agentic artificial intelligence, generative AI and quantum computing paradigms of predicting the risk of osteoporosis. We analyse two classical ML models (Random Forest, Gradient Boosting), two agentic AI models (GPT-4 Agentic, Claude Opus Agentic), two generative AI models (Gemini Pro, LLaMA 3), and two quantum ML models (VQE-based, QAOA-based) on a sample of $\mathbf{2, 1 8 6}$ patients with $\mathbf{4 4}$ clinical and biochemical variables including demographic, anthropometry, blood pressure variables, lifestyle variables, complete blood count variables, liver and renal variables, lipid variables, and bone-related miner Our new hybrid ensemble architecture is based on the superior performance of weighted fusion of predictions, in which the generative AI models will demonstrate excellent results: Gemini Pro with a 86.30% accuracy and $90.85 \% \mathrm{F} 1$-score, and quantum models have a competitive performance with VQE with a 85.62% accuracy. The suggested framework combines various computational paradigms, which offer strong and clinically applicable early detection functions, which will yield a…
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