| Autor: |
Natratanon Kanraweekultana, Sajjaporn Waijanya, Nuttachot Promrit, Undaman Nopnapaporn, Apisada Korsanan, Sansanee Poolphol |
| Předmět: |
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| Zdroj: |
Engineering & Applied Science Research; Jan/Feb2024, Vol. 51 Issue 1, p11-21, 11p |
| Abstrakt: |
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 depressionrelated 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 FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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