Sleep as a predictor of depression level using Naïve Bayes / Nur Syakinah Md Roduan

The percentage of peoples having depression nowadays is said to be inclining. However, many of the patients do not even realize that they are having major depressive disorder. Busy with abundance of works and not having any time to seek a doctor for check-up may worsen the patient condition. So, a p...

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Main Author:	Md Roduan, Nur Syakinah
Format:	Thesis
Language:	English
Published:	2017
Online Access:	https://ir.uitm.edu.my/id/eprint/18241/2/TD_NUR%20SYAKINAH%20MD%20RODUAN%20CS%2017_5.pdf
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id	my-uitm-ir.18241
record_format	uketd_dc
spelling	my-uitm-ir.182412019-02-28T02:32:51Z Sleep as a predictor of depression level using Naïve Bayes / Nur Syakinah Md Roduan 2017 Md Roduan, Nur Syakinah The percentage of peoples having depression nowadays is said to be inclining. However, many of the patients do not even realize that they are having major depressive disorder. Busy with abundance of works and not having any time to seek a doctor for check-up may worsen the patient condition. So, a prediction system was developed to predict students' level of depression based on their sleep behaviors that uses Naïve Bayes method, which implement Artificial Intelligence (AI) as result of survey conducted to the target user proved that majority of them need a system that can predict depression level. Five independent variables which are insomnia, amount of sleep (hours), overall sleep quality, sleep onset latency, and number of awakening per sleep that has been identified as the most-used variables in many previous research are used for the prediction model. The target subject to realize the objectives of the prediction system are students under the Faculty of Computer and Mathematical Sciences in UiTM Jasin who is currently in the 6th semester. From 150 total data collected, 80% of them were used as training data, and 20% were for the new data to be tested. A total of 31 prediction models were produced and tested. All 31 models are to predict the students' depression level, which include normal, mild, borderline, moderate, and severe depression gave an average of 51.075% accuracy for 120 training data and average of 37.527% accuracy for the 30 new data collected through questionnaires to the subject. Agile methodology is used throughout the development of the system to ensure that this project work properly according to plan. Functionality testing are also done to make sure that the system is working properly without having any error. In conclusion, this research demonstrated that Naïve Bayes method could be used to predict the level of depression. Future work on this subject should improve the findings by modifying the variables used and/or by using other methods in term of data collection or the algorithm itself. 2017 Thesis https://ir.uitm.edu.my/id/eprint/18241/ https://ir.uitm.edu.my/id/eprint/18241/2/TD_NUR%20SYAKINAH%20MD%20RODUAN%20CS%2017_5.pdf text en public dphil degree Universiti Teknologi MARA Faculty of Computer and Mathematical Sciences
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
description	The percentage of peoples having depression nowadays is said to be inclining. However, many of the patients do not even realize that they are having major depressive disorder. Busy with abundance of works and not having any time to seek a doctor for check-up may worsen the patient condition. So, a prediction system was developed to predict students' level of depression based on their sleep behaviors that uses Naïve Bayes method, which implement Artificial Intelligence (AI) as result of survey conducted to the target user proved that majority of them need a system that can predict depression level. Five independent variables which are insomnia, amount of sleep (hours), overall sleep quality, sleep onset latency, and number of awakening per sleep that has been identified as the most-used variables in many previous research are used for the prediction model. The target subject to realize the objectives of the prediction system are students under the Faculty of Computer and Mathematical Sciences in UiTM Jasin who is currently in the 6th semester. From 150 total data collected, 80% of them were used as training data, and 20% were for the new data to be tested. A total of 31 prediction models were produced and tested. All 31 models are to predict the students' depression level, which include normal, mild, borderline, moderate, and severe depression gave an average of 51.075% accuracy for 120 training data and average of 37.527% accuracy for the 30 new data collected through questionnaires to the subject. Agile methodology is used throughout the development of the system to ensure that this project work properly according to plan. Functionality testing are also done to make sure that the system is working properly without having any error. In conclusion, this research demonstrated that Naïve Bayes method could be used to predict the level of depression. Future work on this subject should improve the findings by modifying the variables used and/or by using other methods in term of data collection or the algorithm itself.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Bachelor degree
author	Md Roduan, Nur Syakinah
spellingShingle	Md Roduan, Nur Syakinah Sleep as a predictor of depression level using Naïve Bayes / Nur Syakinah Md Roduan
author_facet	Md Roduan, Nur Syakinah
author_sort	Md Roduan, Nur Syakinah
title	Sleep as a predictor of depression level using Naïve Bayes / Nur Syakinah Md Roduan
title_short	Sleep as a predictor of depression level using Naïve Bayes / Nur Syakinah Md Roduan
title_full	Sleep as a predictor of depression level using Naïve Bayes / Nur Syakinah Md Roduan
title_fullStr	Sleep as a predictor of depression level using Naïve Bayes / Nur Syakinah Md Roduan
title_full_unstemmed	Sleep as a predictor of depression level using Naïve Bayes / Nur Syakinah Md Roduan
title_sort	sleep as a predictor of depression level using naïve bayes / nur syakinah md roduan
granting_institution	Universiti Teknologi MARA
granting_department	Faculty of Computer and Mathematical Sciences
publishDate	2017
url	https://ir.uitm.edu.my/id/eprint/18241/2/TD_NUR%20SYAKINAH%20MD%20RODUAN%20CS%2017_5.pdf
_version_	1783733640917680128

Sleep as a predictor of depression level using Naïve Bayes / Nur Syakinah Md Roduan

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