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ผศ.ดร.ณัฐรฐนนท์ กานต์รวีกุลธนา | Efficiencycomparison Classification models PHQ-9

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Collection	RMUTK Research Repository (RMUTK IR) วารสารวิชาการ — e-Journal Articles
ID RMUTK Digital	RMUTK000019
Title
Alternative title	Comparison of capability of data classification models to predict consistent results for depression analysis based on user-behaviour tracking and facial expression recognition during PHQ-9 assessment
Authors	ผศ.ดร.ณัฐรฐนนท์ กานต์รวีกุลธนา ผู้แต่งหลัก ผศ.ดร.สัจจาภรณ์ ไวจรรยา (มหาวิทยาลัยศิลปากร) ผศ.ดร.ณัฐโชติ พรหมฤทธิ์ (มหาวิทยาลัยศิลปากร) นายอันดามัน นพนภาพร (มหาวิทยาลัยศิลปากร) นางสาวอภิษฎา กอสนาน (กลุ่มงานจิตเวชและยาเสพติด โรงพยาบาลสมเด็จพระพุทธเลิศหล้า) นางศันสนีย์ พูลผล (กลุ่มงานจิตเวชและยาเสพติด โรงพยาบาลสมเด็จพระพุทธเลิศหล้า)
Faculty	คณะบริหารธุรกิจ
Journal Title	Engineering and Applied Science Research (EASR)
ISSN	2539-6218 (Online)
Volume / Issue / Pages	Vol.51 \| No.1 \| pp.11-21
Published	2023-12-14
Year	2566
Level	ระดับนานาชาติ (SCOPUS)
Quartile	Q3
DOI	https://www.tci-thaijo.org/index.php/easr/index
Funding Source	-
Abstract	This research compares the capability of data classification models to predict consistent results for a subject’s depression potentiality, track the subject behaviour and recognise facial expressions during PHQ-9 assessments. This research is motivated by the necessity for depression screening and diagnosis, which traditionally relies on observations by experienced physicians or clinical psychologists of symptoms in conjunction with data from questionnaires. However, the field still requires a suitable technological approach that gives more accurate and consistent results. All data used in the present research were collected by combining technologies and compared by using classification models, the goal being to find the machine-learning model that most accurately predicts consistent results for the subjects’ PHQ-9 assessment, behaviours and emotions. The subjects were screened by clinical psychologists and divided into three groups: (i) subjects suffering from depression but not receiving treatment (undertreated subjects), (ii) subjects undergoing depression treatment (subjects undergoing treatment) and (iii) subjects without depression disorder (normal subjects). Related studies have compared the accuracy of classification models to one another. The four most frequently applied classification models in depression-related studies are (i) decision tree (ii) support vector machine, (iii) naïve Bayes and (iv) neural network. All models were analysed, designed and developed before being tested experimentally. The accuracy of the experimental results was tested by using the data analysis tool RapidMiner Studio. The results show that the decision tree model is not only the most accurate for predicting depression potentiality, tracking behaviour and recognising facial expressions during PHQ-9 assessments but also the most suitable.
Abstract (EN)	This research compares the capability of data classification models to predict consistent results for a subject’s depression potentiality, track the subject behaviour and recognise facial expressions during PHQ-9 assessments. This research is motivated by the necessity for depression screening and diagnosis, which traditionally relies on observations by experienced physicians or clinical psychologists of symptoms in conjunction with data from questionnaires. However, the field still requires a suitable technological approach that gives more accurate and consistent results. All data used in the present research were collected by combining technologies and compared by using classification models, the goal being to find the machine-learning model that most accurately predicts consistent results for the subjects’ PHQ-9 assessment, behaviours and emotions. The subjects were screened by clinical psychologists and divided into three groups: (i) subjects suffering from depression but not receiving treatment (undertreated subjects), (ii) subjects undergoing depression treatment (subjects undergoing treatment) and (iii) subjects without depression disorder (normal subjects). Related studies have compared the accuracy of classification models to one another. The four most frequently applied classification models in depression-related studies are (i) decision tree (ii) support vector machine, (iii) naïve Bayes and (iv) neural network. All models were analysed, designed and developed before being tested experimentally. The accuracy of the experimental results was tested by using the data analysis tool RapidMiner Studio. The results show that the decision tree model is not only the most accurate for predicting depression potentiality, tracking behaviour and recognising facial expressions during PHQ-9 assessments but also the most suitable.
Keywords (TH)	Efficiencycomparison Classification models PHQ-9 Depression
Keywords (EN)	Efficiencycomparison Classification models PHQ-9 Depression
Access Level	Open Access (เปิดสาธารณะ)

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