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| Article name |
Development Of A Model For Screening Patients With Dementia In The Early Stages with Machine Learning Techniques
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| Article type |
Research article
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| Authors |
Lersak Phothong(1), Chutimon Jampee(1), Phenphitcha Chantho(1) and Prapaporn Chubsuwan(1*)
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| Office |
Department of Business Computer, Mahasarakham Business School, Mahasarakham University(1) *Corresponding author: prapaporn.c@mbs.msu.ac.th
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| Journal name |
Vol. 11 No.3 (2025): September -
December
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| Abstract |
Accurate early detection of dementia is crucial for effective management. This study aims to develop and compare the performance of three machine learning models: Random Forest (RF), Logistic Regression (LR), and Support Vector Machine (SVM) for screening brain conditions. Using a dataset of 1,842 samples, including dementia data and 18 health factors, the data was analyzed, and models were developed according to the standard CRISP-DM data mining processes. Performance was assessed using accuracy, F-measure, sensitivity, and specificity. The results showed that LR had the highest accuracy (94.03%), followed by RF (93.60%) and SVM (92.56%). LR also demonstrated superior F-measure (96.76%), sensitivity (99.51%), and specificity (47.63%). This study indicates the potential of LR in dementia screening, outperforming RF and SVM in terms of accuracy, efficiency, and balanced classification. These findings support the development of machine learning-based tools to aid clinical decision-making in dementia diagnosis and effective treatment planning.
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| Keywords |
Dementia; Machine Learning; Logistic Regression; Classification Performance; Dementia Screening
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| Page number |
1-18
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| ISSN |
ISSN 3027-7280 (Online)
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| DOI |
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| ORCID_ID |
0009-0006-7104-9831
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| Article file |
https://mitij.mju.ac.th/ARTICLE/R68020.pdf
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