Transformative Randomized Decision Trees for Sleep Disorder Classification from Health Life-Style Data

Abstract

Sleep disorders, such as insomnia and sleep apnea, impact a substantial portion of the global population, with insomnia affecting approximately 10% of adults and sleep apnea influencing up to 3%. Despite these significant statistics, existing diagnostic approaches face challenges such as incomplete datasets and suboptimal classification accuracy. Traditional methods often struggle with differentiating between various sleep disorders due to limitations in feature extraction and classification techniques. To address these challenges, a novel Sleep Disorders Classification (SDC) framework is proposed. This framework incorporates advanced preprocessing techniques to enhance data quality and uses Incremental Eigen Values Analysis (IEVA) for efficient feature extraction, which dynamically reduces the dimensionality of the dataset. The framework also employs Randomized Decision Trees (RDT) classification to accurately distinguish between insomnia, sleep apnea, and normal sleep conditions. By integrating these advanced methods, the SDC framework aims to improve the accuracy and reliability of sleep disorder diagnoses.