Sleeping Disorder Prediction

Abstract

Disruptions in normal sleep patterns present significant challenges to physical health, cognitive function, and overall well-being. This research introduces an innovative computational framework for identifying sleep abnormalities through multivariate analysis of patient data. Our approach employs two distinct algorithmic strategies: Recursive Ensemble Learning (REL) and Multivariate Gaussian Differentiation (MGD). These methods were selected for their complementary strengths in pattern recognition and probabilistic classification. Performance evaluation reveals that the REL technique achieved remarkable accuracy (92.3%) compared to MGD (87.6%). By analyzing correlations between nighttime behaviors, lifestyle variables, and physiological readings, our system can identify individuals requiring clinical intervention. This framework significantly enhances diagnostic capabilities by providing quantitative risk assessments, allowing healthcare providers to implement targeted interventions more effectively.

Introduction

Sleep disorders affect 50-70 million Americans yearly, impacting daily functioning and health. Traditional diagnosis relies on costly, resource-intensive clinical tests like Polysomnography (PSG) and subjective self-reports, which have limitations in accessibility and accuracy. Existing computational models have had limited success due to inadequate feature representation and inability to capture complex patterns.

This research proposes a novel computational framework combining Gradient Boosting Classifier (an ensemble machine learning method) and Quadratic Discriminant Analysis (a probabilistic classifier) to improve accuracy and efficiency in sleep disorder prediction. The system integrates multidimensional features including sleep metrics, behavioral factors (e.g., tobacco, alcohol), psychological states (stress, anxiety), and physiological conditions (diabetes, hypertension).

The workflow involves data collection, preprocessing, feature engineering, and parallel training of the two models. Gradient Boosting demonstrated higher accuracy (92%) and better handling of complex data relationships, while QDA offers faster, computationally efficient predictions. The model outputs both categorical diagnoses and quantitative risk assessments and supports continuous learning for improved performance.

The framework is designed for deployment on mobile and web platforms, enhancing accessibility for broader screening. Future work aims to integrate wearable data and hybrid modeling techniques to further optimize sleep disorder detection and management.

Conclusion

Our research demonstrates the substantial potential of advanced machine learning techniques in sleep disorder prediction, with the Gradient Boosting Classifier achieving 92% accuracy compared to 88% for Quadratic Discriminant Analysis. Both models demonstrated strong predictive capabilities, with Gradient Boosting exhibiting particular strength in handling complex feature interactions and dataset variability. The performance metrics showed high precision and recall values across both approaches, with Gradient Boosting demonstrating marginally superior recall characteristics, indicating enhanced capability in identifying individuals requiring intervention. While QDA showed slightly reduced accuracy, it provided computational efficiency advantages with faster inference times and implementation simplicity. The comprehensive evaluation using F1-score and ROC-AUC consistently favored Gradient Boosting across measurement parameters, highlighting the operational tradeoff between predictive accuracy and computational efficiency in practical applications

References

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Copyright

Copyright © 2025 P Hemanth Kumar, R H Hemanth Kumar , Nithin Naidu S P, Sahebgoud Danagond , R S Geethanjali. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.