Sleep Disorder Diagnosis Using Ensemble Learning Approaches

Ms Mayuri R. Nahata; Dr. Khyati R. Nirmal

doi:10.70528/IJLRP.v6.i12.1870

Sleep Disorder Diagnosis Using Ensemble Learning Approaches

Author(s)	Ms Mayuri R. Nahata, Dr. Khyati R. Nirmal
Country	India
Abstract	Sleep disorders can be life threatening and very health related. Early warning is valuable, but the traditional methods suggest sleep data analysis performed manually by specialists and, therefore, is time-consuming and is not always stable. This paper is a review of the way machine learning and deep learning algorithms have been used to improve automatic diagnosis of sleep disorders. These models are discussed on a dataset of 400 records with 13 features; they include k-nearest neighbors, support vector machine, decision tree, random forest, and artificial neural network. Moreover, the unsupervised learning methods, including K-Means Clustering are used to identify the hidden patterns and decrease the number of dimensions of data to improve the performance of the model. The different models have varying precision and we develop a hybrid model to further enhance the performance by developing a user-friendly web application that is developed on Django and SQLite where the patients and medical professionals can easily enroll, establish tests and also look at the results.
Keywords	Sleep disorders, Machine learning, Hybrid model, Django, Healthcare.
Field	Engineering
Published In	Volume 6, Issue 12, December 2025
Published On	2025-12-16
Cite This	Sleep Disorder Diagnosis Using Ensemble Learning Approaches - Ms Mayuri R. Nahata, Dr. Khyati R. Nirmal - IJLRP Volume 6, Issue 12, December 2025. DOI 10.70528/IJLRP.v6.i12.1870
DOI	https://doi.org/10.70528/IJLRP.v6.i12.1870
Short DOI	https://doi.org/hbfr2d

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Sleep Disorder Diagnosis Using Ensemble Learning Approaches

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