Applying Machine Learning Algorithm For The Classification Of Sleep Disorders
DOI:
https://doi.org/10.63665/IJAICE.0203.06Keywords:
Sleep Disorders, Insomnia, Sleep Apnea, Machine Learning, Random Forest, Classification, Demographic Features, , Sleep Patterns, Lifestyle Factors, Vital Health Data, Feature Importance, ROC AnalysisAbstract
Sleep disorders such as Insomnia and Sleep Apnea significantly affect health, well-being and daily functioning. The project develops a machine-learning based classification system that predicts sleep-disorder categories from demographic, sleep, lifestyle and vital-health information. The source thesis describes a dataset of 400 samples with 13 relevant features and three target classes: Insomnia, None and Sleep Apnea.
The proposed approach uses a Random Forest classifier. The model is selected because ensemble learning reduces the risk of overfitting, supports mixed data, requires comparatively modest computational resources and provides feature-importance information. The thesis reports an accuracy of 95% and evaluates the model using accuracy, precision, recall, F1-score, confusion matrix and ROC analysis.
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