Predictive Analysis of Sleep Disorders Using Machine Learning: A Comprehensive Analysis

Title

Predictive Analysis of Sleep Disorders Using Machine Learning: A Comprehensive Analysis

Creator

Malik N.; Gupta V.; Sharma V.

Description

The diagnosis of sleep disorders often relies on subjective patient reports, sleep diaries, and potentially cumbersome polysomnography (PSG) tests. However, these methods have limitations such as subjectivity, sleep diaries require meticulous effort, and expensive PSG tests are expensive, resource-intensive, and may not accurately capture sleep patterns in a non-clinical setting. Sleep disorders pose significant health risks and can impair overall well-being. Predictive analysis plays a crucial role in identifying individuals at risk of developing sleep disorders, enabling timely interventions and personalized treatment plans. In this paper, a comparative analysis of regression and classification models for sleep disorders prediction using machine learning (ML) techniques on insomnia and sleep apnea are discussed. Through extensive experimentation and comparative analysis, XGBoost and AdaBoost demonstrated as the most effective predictive models for insomnia and sleep apnea. AdaBoost and XGBoost classifiers are displaying 93.49% and 92.73% respectively. It is therefore possible to draw the conclusion that AdaBoost and XGBoost are doing well based on the findings as a whole, as indicated by the results. Our findings contribute to advancing the understanding and application of ML techniques in sleep disorder prediction, paving the way for more accurate and timely diagnosis based on ML techniques and personalized interventions in clinical practices. 2024 IEEE.

Source

TQCEBT 2024 - 2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies 2024

Date

2024-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

AdaBoost; classification; machine learning; regression; sleep disorders; XGBoost

Coverage

Malik N., Christ Deemed to Be University, India; Gupta V., Christ Deemed to Be University, India; Sharma V., Christ Deemed to Be University, India

Rights

Restricted Access

Relation

ISBN: 979-835038427-7

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/TQCEBT59414.2024.10545120

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204489521&doi=10.1109%2fTQCEBT59414.2024.10545120&partnerID=40&md5=18fc6931ff30743548ec970940b4486a