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Machine Learning Approach for Insomnia Early Diagnosis in Perspective of Bangladesh

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dc.contributor.author Tasfia, Orin
dc.date.accessioned 2024-08-19T06:01:16Z
dc.date.available 2024-08-19T06:01:16Z
dc.date.issued 2024-01-24
dc.identifier.uri http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/13065
dc.description.abstract This research addresses the early detection of insomnia in Bangladesh, focusing on young adults affected by extensive phone and social media use. Using machine learning techniques like Logistic Regression, SVM, Decision Tree, Random Forest, XGBoost, CatBoost, Naive Bayes, and Light GBM, the study analyzed survey data from university students. The data processing was conducted using Python and Pandas, with null values handled carefully. Psychiatric validation was included. The models, especially Logistic Regression and CatBoost, achieved high accuracy (1.0 in Accuracy, ROC, AUC), suggesting a strong link between survey symptoms and insomnia. This approach, novel in Bangladesh, demonstrates machine learning's potential in mental health diagnosis, offering a cost-effective alternative to traditional methods. The study suggests further research to expand datasets and tailor models for diverse demographics, integrating these findings into public health policies en_US
dc.publisher Daffodil International University en_US
dc.subject Machine Learning en_US
dc.subject Insomnia Diagnosis en_US
dc.subject Medical Diagnosis en_US
dc.subject Artificial Intelligence en_US
dc.subject Healthcare Technology en_US
dc.title Machine Learning Approach for Insomnia Early Diagnosis in Perspective of Bangladesh en_US
dc.type Other en_US


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