Captivating Insights into Sleep Health: Orchestrating Lifestyle Features and Predictive Models through Computational Intelligence

Abstract

This study develops an interpretable Machine Learning (ML) framework with enhanced feature engineering to predict sleep disorders, achieving 93% accuracy and improving over prior models. The main objective of this work is to enhance the scientific rigor of sleep research and computational intelligence applications by providing insights into evidence-based interventions and optimizing sleep quality through customized lifestyle modifications. The methodology includes careful dataset selection, preprocessing to manage missing data and ensure integrity, and comprehensive feature engineering to accounting for the temporal aspects of sleep patterns. Multiple machine learning models are developed, including supervised and unsupervised approaches such as clustering algorithms, SVM, Random Forests, and Neural Networks. Evaluation metrics—accuracy, precision, recall, and F1-score—are cross-validated to ensure model robustness and prevent overfitting. Visualization tools, including heatmaps, scatter plots, and dimensionality reduction techniques, are employed to interpret the relationships between lifestyle factors and sleep health outcomes. Ethical considerations—such as data privacy, confidentiality, and the responsible interpretation of results—are upheld throughout the investigation.