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A Hybrid Swarm Intelligence and Machine Learning Approach for Predictive Analysis of Sleep Disorders
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Author(s): G. Surekha (Bharath Institute of Higher Education and Research, India)and Edwin Shalom Soji (Bharath Institute of Higher Education and Research, India)
Copyright: 2026
Pages: 20
Source title:
AI in Health and Human-Centric Systems
Source Author(s)/Editor(s): Ahmed J. Obaid (University of Kufa, Iraq), Muthmainnah (Universitas Al Asyariah Mandar, Indonesia), S. Suman Rajest (Dhaanish Ahmed College of Engineering, India)and Michael Baron (Analytics Institute of Australia, Australia)
DOI: 10.4018/979-8-3373-6796-5.ch001
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Abstract
Millions of patients suffer from sleep disorders, which is a global problem. It leads to severe health problems such as cardiovascular diseases, depression, and impairment of cognitive activity. Traditional polysomnography is expensive and time-consuming. Clinical supervision is required during the diagnosis process; hence, the method needs to be changed to enhance the accuracy of the diagnosis. By reducing dimensionality and optimizing models with the help of swarm intelligence algorithms, such as PSO and ACO, feature selection feeds the optimized features into numerous machine learning classifiers for training, including SVM, random forests, deep neural networks, and many more. Additionally, validation on benchmark sleep disorder datasets demonstrates better classification accuracy, sensitivity, and specificity compared to traditional methods. This also suggests that a hybrid model, combining swarm intelligence for feature optimization with machine learning classification, exhibits substantial predictive accuracy with minimal computational complexity.
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