Abstract :

Sleep Disorders, In Particular Sleep Apnoea, Affect Peoples Health And This Demonstrates The Need For A Correct Diagnosis. Sleep Experts, However, Have To Resort To Complicated And Time Consuming Techniques To Manually Categorise The Various Sleep Stages. This Work Offers A ML Classification Model Using The Sleep Disorder Data, Which Is Publicly Accessible, And Has 400 Records And 13 Attributes. Various DL/ML Methods Are Reviewed And Tested For Accurate Sleep Disorder Diagnosis. The Data Points That Are Beneficial For Identifying Patterns Are Related To Lifestyle Indicators And Sleep Health Attributes, Which Can Be Indicative Of Any Existing Sleep-related Conditions. From The Evaluation Of The Models, It Was Observed That The Models With The Best Results Are The Bagged Models And In Particular The VC With RF And DT. The Algorithms Accuracy, Precision, Recall And F1-score Were 0.973 Demonstrating The Reliability And Effectiveness Of The Technique For Sleep Disorder Categorisation. The Results In This Paper Indicate That The Proposed ML Methods Offer A Potential Way To Develop More Intelligent, Faster And Accurate Sleep Disorders Diagnosis That Can Enhance The Possibilities Of The Physicians Decision-making Process And Patients Situation.

Published:

22-10-2026

Issue:

Vol. 26 No. 10 (2026)

Page Nos:

89-95

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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