Classification of sleep disorders using support vector machine

Nuraeni, Nenden and Faisal, Muhammad ORCID: https://orcid.org/0000-0003-4884-7254 (2025) Classification of sleep disorders using support vector machine. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 10 (1). pp. 63-74. ISSN 2503-2267

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Abstract

Sleep disorders become a severe concern in our busy modern lifestyles, which are often overlooked and can cause significant negative impacts on an individual's health and quality of life. This research explores the implementation of machine learning, specifically Support Vector Machine, to facilitate quick and accurate sleep disorder diagnosis. Data shows that sleep deprivation or disturbed sleep is becoming common in society, with 62% of the adult population experiencing dissatisfaction with their sleep quality. This has a significant economic impact and affects the health and productivity sectors. This study uses Kaggle Sleep Health and Lifestyle dataset of 400 data samples, applying Support Vector Machine to classify sleep disorders using three testing scenarios. The results showed an accuracy rate of 92%, confirming that Support Vector Machine can potentially improve the diagnosis of sleep disorders, enabling early intervention and better treatment for patients. Thus, this research contributes to understanding and treating sleep disorders, improving people's overall quality of life.

Item Type:	Journal Article
Keywords:	Sleep Disorder; Insomnia; Sleep Disorder Classification; Sleep Apnea; Support Vector Machine
Subjects:	08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080108 Neural, Evolutionary and Fuzzy Computation
Divisions:	Faculty of Technology > Department of Informatics Engineering
Depositing User:	Muhammad Faisal
Date Deposited:	07 Feb 2025 15:36

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