Predicting The Type Of Sleep Disorders Using Data Mining Classification Techniques

Abstract

A fundamental human need, sleep is important for both physical and mental wellbeing. Our brain needs sleep to work correctly. Numerous negative effects may result from inadequate sleep or sleep of low quality. Conditions called sleep disorders cause changes in how we sleep. Our general health, safety, and enjoyment of life may be impacted by a sleep disturbance. Lack of sleep can develop many health issues. Insomnia, sleep apnea, restless legs syndrome, narcolepsy, parasomnias, and hypersomnia are only a few examples of the various forms of sleep disorders. Recent studies says that the obstructive sleep apnea risk and symptoms among middle-aged Saudi men and women and found they that 3 of every 10 Saudi men and 4 of every 10 Saudi women are at high risk for obstructive sleep apnea^[1]. A simple pre-coded questionnaire will be developed, and data is collected from 151household students from the Public Health College in Jazan. The questionnaire includes socio demographic factors, sleep symptoms and behavioural data. The data science is an interdisciplinary field which is used to extract the knowledge from huge data. Hence, it plays a vital role to predict the type of sleep disorder. This paper focuses on how Data Mining classification helps to analyze the sleep disorder dataset with Random tree and One R. These algorithms are implemented using Weka tool. As a result, the classifiers performance was evaluated based on factors like confusion matrix. In our research we found that the classifier Random tree is giving more accuracy, minimum time taken to construct model and less error rate than One R classifier.