Sleep disorder In Quatum computing

The growing prevalence of sleep disorders has become a significant concern for global health, affecting individuals' quality of life and overall well-being. This study explores the application of Quantum Computing, specifically Variational Quantum Classifier (VQC) and Quantum Support Vector Machine (QSVM), to analyze and predict sleep disorders using the Sleep Health and Lifestyle Dataset. The dataset comprises 400 individuals, with key variables such as sleep duration, quality, physical activity, stress levels, cardiovascular health indicators (blood pressure, heart rate), and the presence of sleep disorders like Insomnia and Sleep Apnea. Through the implementation of VQC, a quantum-enhanced model, the study aims to improve prediction accuracy compared to traditional machine learning methods. By leveraging quantum computing techniques, this research seeks to provide deeper insights into the complex relationship between lifestyle factors and sleep disorders, thus contributing to the development of advanced, data-driven approaches for early diagnosis and intervention in sleep-related health issues.

Keywords: Quantum Computing, Variational Quantum Classifier (VQC), Quantum Support Vector Machine (QSVM), Sleep Disorders, Sleep Health, Lifestyle Dataset, Sleep Duration, Sleep Quality, Physical Activity, Stress Levels, Cardiovascular Health, Blood Pressure, Heart Rate, Insomnia, Sleep Apnea, Prediction Accuracy, Machine Learning, Quantum-Enhanced Model, Early Diagnosis, Data-Driven Approaches, Intervention, Health Issues.