Advanced Thyroid Disease Prediction Using a Quantum-Deep Hybrid Ensemble (QDHE) Machine Learning Approach

Thyroid illness is a widespread worldwide health issue, frequently identified late due to inadequacies in traditional screening techniques. Current machine learning models have elevated false negative rates, inefficient inference, and inadequate feature selection methodologies. This research introduces the Quantum-Deep Hybrid Ensemble (QDHE) paradigm, which combines quantum-enhanced feature selection with deep learning ensembles for accurate thyroid disease prediction. The suggested approach enhances computational efficiency, lowering the error rate to 1.8% while attaining 98.2% accuracy, 98.0% precision, and 97.9% F1-score. In comparison to baseline models, QDHE demonstrates enhanced scalability and real-time inference (25ms each prediction), finding it appropriate for clinical applications. The hybrid deep learning methodology enhances early-stage disease identification, minimising diagnostic delays. Future endeavours will concentrate on enhancing quantum-driven models for wider applications in medicine. This study greatly progresses medical AI, improving predicted precision, dependability, and efficacy in healthcare.