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AI driven quantum machine learning for predictive healthcare analytics
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AI
DOI：10.1016/j.bspc.2026.109937.png)
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Abstract
En 中文
Safeguarding of mental health is critical for our overall health because without neurological health we can never achieve well-being. We focus on important issue of classifying, predicting, and diagnosing the two life-disrupting neurological conditions, namely brain tumor and brain stroke. The objective in Artificial Intelligence (AI) is to secure the intricate neural healing pathways via the relief of advanced machine learning algorithms along with quantum computations using high performance computation engines. The focus is to improve the real life application areas using engineering for early detection and prediction of brain disorders such as tumors and strokes by providing accurate results. The paper elaborates the use of quantum machine learning algorithms on exponentially faster machines in neurological databases, refine infrastructure models for secure data transfer and create the interface for user- friendly precised diagnosis. The interface developed will be used by the users for tests and diagnosis, enabling them to reduce human mistakes. The peculiar approach of this research is to employ Quantum based machine learning and deep learning algorithms on High Performance Computing (HPC) network. It shows the variation in results gradually increasing, achieving the accuracy and precision for the early detection and diagnosis of the disease. Dataset resulted in remarkable accuracy in detecting and diagnosing brain tumor (98 %) and brain stroke (94.7 %) with the help of advanced quantum machine learning algorithms.
Keywords:
Quantum machine learning
High Performance Computing
Brain tumor detection
Brain stroke diagnosis
Predictive healthcare analytics
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