Quantum-Inspired Optimization Framework for Healthcare Predictive Modeling

Abstract

The increasing complexity of healthcare datasets demands advanced optimization techniques to improve predictive modeling performance. This paper presents a Quantum-Inspired Optimization Framework for Healthcare Predictive Modeling, designed to enhance feature selection and model accuracy in complex medical datasets. The proposed approach leverages quantum-inspired algorithms combined with classical machine learning models to explore high-dimensional search spaces more effectively. Techniques such as quantum-inspired evolutionary algorithms and hybrid optimization strategies are employed to identify optimal feature subsets and model parameters. The framework is integrated with deep learning and ensemble learning methods to improve prediction accuracy for diseases such as cardiovascular conditions and diabetes. Additionally, adaptive mechanisms are incorporated to dynamically refine the optimization process based on evolving data patterns. Experimental results demonstrate that the proposed framework outperforms traditional optimization methods in terms of convergence speed, accuracy, and robustness. Integration with prior research in healthcare analytics further strengthens system reliability and generalization. The study highlights the potential of quantum-inspired approaches in advancing next-generation healthcare predictive systems.