Sleeping Disorder Prediction using Machine Learning

BHARATH; HARI PRASAD; HAFIZUN; MAITRI; M.NARASIMHA YADAV

Sleeping Disorder Prediction using Machine Learning

Author(s)	Mr. BHARATH, HARI PRASAD, HAFIZUN, MAITRI, M.NARASIMHA YADAV
Country	India
Abstract	Sleep disorders have been identified as one of the most common health-related problems that cut across all age groups. Insomnia and sleep apnea are some of the sleep disorders that have been identified to have effects on physical health, mental health, and work performance. The conventional method of diagnosing sleep disorders is time-consuming, expensive, and requires expert monitoring. Due to these problems, there is a growing need for an accurate and cost-effective system that can predict sleeping disorders. Machine learning algorithms are an efficient way of processing large amounts of information related to sleep and identifying hidden patterns that cannot be easily identified through human observation. The project proposes to develop a machine learning system for predicting sleeping disorders using physiological and behavioral sleep information. The system collects information such as the duration of sleep, heart rate, breathing patterns, body movement, oxygen saturation levels, and lifestyle information. The collected information is preprocessed to remove noise, handle missing values, and normalize the variables to improve the efficiency of the model. Feature extraction and selection techniques are used to identify the most important variables that affect the prediction of sleep disorders. After preprocessing, various machine learning algorithms are used to train. Techniques of feature extraction and selection are used to identify the most important variables that contribute to the prediction of sleep disorders. After the preprocessing stage, various machine learning algorithms are used to train the model. Some of the algorithms used include Artificial Neural Networks (ANN), Support Vector Machines (SVM), Decision Trees, and Random Forest Classifiers to analyze the sleep patterns and predict sleeping disorders. Of these algorithms, ANN is the most effective due to its ability to learn non-linear relationships between variables.
Keywords	performance evaluation, feature extraction, Machine learning, predictive models, data models, sleep apnea, Support Vector Machines (SVM), Decision Trees, and Random Forest.
Field	Engineering
Published In	Volume 17, Issue 2, April-June 2026
Published On	2026-04-04

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International Journal on Science and Technology

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Sleeping Disorder Prediction using Machine Learning

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