Classification and Evaluation of Sleep Disorders Using Random Forest Algorithm in Health and Lifestyle Dataset

Abstract

Sleep is a fundamental aspect of human life, accounting for approximately one-third of our existence and playing a crucial role in the restoration of physical health and overall quality of life. However, poor sleep quality can interfere with these critical restorative processes, leading to disorders such as apnoea and insomnia. These conditions not only impair daily performance but also have long-term health consequences. Furthermore, the challenges imposed by modern lifestyles have increased the prevalence of these sleep disorders, emphasizing the need for effective diagnostic tools. This research aims to harness the capabilities of Machine Learning (ML), specifically the Random Forest algorithm, to detect and analyse patterns indicative of sleep disorders in collected data sets. Random Forest is particularly suited for this task due to its ability to manage complex data sets by building multiple decision trees, thus creating a comprehensive and robust model for classifying sleep disorders. The findings of the study are promising, showing that the Random Forest algorithm can achieve a high level of accuracy in sleep disorder detection. The model demonstrated a test accuracy rate of 97.33%, with a precision of 96%, and a recall rate of 100%. Additionally, it achieved an F1-Score of 98% and a Kappa Score of 0.945, validating the reliability of this algorithm in producing precise classifications. This research offers significant insights into the patterns of sleep disorders and contributes to the development of targeted interventions aimed at improving sleep quality. Ultimately, this could significantly enhance the quality of life for individuals suffering from sleep disorders.