Prediction of sleep disorders using Novel decision support neutrosophic based machine learning models

Sleep disorders significantly impact human health, productivity, and overall quality of life. Early diagnosis and prediction of these disorders are crucial for effective treatment. This study introduces a novel decision support system utilizing a neutrosophic machine learning prediction model to enhance the accuracy and reliability of sleep disorder diagnosis. Unlike traditional machine learning approaches, our model integrates neutrosophic logic, which considers three values—truth, indeterminacy, and falsity—to effectively handle uncertainty and inconsistencies in sleep-related data. The proposed model processes diverse patient information, including demographics, clinical parameters, and polysomnography details, ensuring comprehensive analysis. Experimental results demonstrate that our approach surpasses conventional machine learning methods in predictive accuracy, robustness, and interpretability. Furthermore, this research provides an advanced framework for clinicians to assess potential sleep disorders while accommodating inherent uncertainties in medical data. The study highlights the impact of neutrosophic machine learning in healthcare decision support systems and outlines potential avenues for future research.