Early and accurate disease prediction is a major challenge in modern healthcare systems. Delayed diagnosis often leads to higher treatment costs and lower patient survival rates. Artificial Intelligence (AI) and Machine Learning (ML) techniques are widely used to help with medical decision-making by analyzing complex healthcare datasets. However, traditional machine learning models often face issues with inefficient feature selection, poor hyperparameter tuning, and slow convergence during optimization. This is especially true when working with high-dimensional medical data. To tackle these challenges, this paper presents an AI-based disease prediction framework that uses quantum-inspired optimization techniques. This approach combines classical machine learning classifiers with optimization strategies based on quantum computing principles, such as probabilistic state representation and superposition-based search. These quantum-inspired methods allow for efficient exploration of the solution space, which leads to better feature selection and optimized model parameters. We evaluate the proposed framework using a publicly available healthcare dataset from Kaggle. We compare the performance of traditional machine learning models and quantum-inspired optimized models using accuracy, precision, recall, and F1-score metrics. The experimental results show that the quantum-inspired optimized model consistently performs better than conventional approaches. This study demonstrates that quantum-inspired optimization provides a practical and scalable solution for improving AI-driven disease prediction systems without the need for actual quantum computing hardware.
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