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@article{193744,
        author = {C Rekha and Marthala Amruthavalli and Basineni Bharath Kumar and Gopidinne Harshitha and Voodi Hemanth},
        title = {SLEEP DISORDER IDENTIFICATION USING MACHINE LEARNING AND DEEP LEARNING MODELS},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {1636-1643},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=193744},
        abstract = {sleep disorders, including sleep apnea, significantly impact human health and overall quality of life. Accurate classification of sleep disorders is essential for effective diagnosis and treatment; however, manual sleepstage classification by experts is often time consuming and prone to error. This study explores the application of machine learning algorithms (MLAs) for sleep disorder classification using the publicly available Sleep Health and Lifestyle Dataset, which comprises 400 records and 13 features related to sleep and daily activities. Conventional MLAs such as k-nearest neighbours, support vector machines, decision trees, and random forests were compared with a deep learning approach based on artificial neural networks (ANN). To enhance model performance, a genetic algorithm was employed to optimise the parameters of each model. Experimental results revealed notable performance variations among the evaluated algorithms. The ANN model demonstrated superior classification capability compared to conventional machine learning methods, achieving strong precision, recall, and F1-score. These findings highlight the potential of optimised deep learning models, particularly ANN, in enabling reliable and efficient sleep disorder classification, thereby contributing to improved healthcare outcomes.},
        keywords = {Machine learning algorithms, deep learning, classification, genetic algorithm},
        month = {March},
        }