| Autor: |
Metarugcheep S; Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, Thailand., Punyabukkana P; Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, Thailand., Wanvarie D; Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand., Hemrungrojn S; Department of Psychiatry, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, Thailand.; Cognitive Fitness and Biopsychological Technology Research Unit, Chulalongkorn University, Bangkok 10330, Thailand., Chunharas C; Cognitive Clinical & Computational Neuroscience Research Unit, Department of Internal Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, Thailand.; Chula Neuroscience Center, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok 10330, Thailand., Pratanwanich PN; Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand.; Chula Intelligent and Complex Systems Research Unit, Chulalongkorn University, Bangkok 10330, Thailand. |
| Abstrakt: |
Mild cognitive impairment (MCI) is an early stage of cognitive decline or memory loss, commonly found among the elderly. A phonemic verbal fluency (PVF) task is a standard cognitive test that participants are asked to produce words starting with given letters, such as "F" in English and "ก" /k/ in Thai. With state-of-the-art machine learning techniques, features extracted from the PVF data have been widely used to detect MCI. The PVF features, including acoustic features, semantic features, and word grouping, have been studied in many languages but not Thai. However, applying the same PVF feature extraction methods used in English to Thai yields unpleasant results due to different language characteristics. This study performs analytical feature extraction on Thai PVF data to classify MCI patients. In particular, we propose novel approaches to extract features based on phonemic clustering (ability to cluster words by phonemes) and switching (ability to shift between clusters) for the Thai PVF data. The comparison results of the three classifiers revealed that the support vector machine performed the best with an area under the receiver operating characteristic curve (AUC) of 0.733 (N = 100). Furthermore, our implemented guidelines extracted efficient features, which support the machine learning models regarding MCI detection on Thai PVF data. |
