TY  - JOUR
AU  - Thanh Phuc, Phan
AU  - Nguyen, Phung-Anh
AU  - Nguyen, Nam Nhat
AU  - Hsu, Min-Huei
AU  - Le, Nguyen Quoc Khanh
AU  - Tran, Quoc-Viet
AU  - Huang, Chih-Wei
AU  - Yang, Hsuan-Chia
AU  - Chen, Cheng-Yu
AU  - Le, Thi Anh Hoa
AU  - Le, Minh Khoi
AU  - Nguyen, Hoang Bac
AU  - Lu, Christine Y
AU  - Hsu, Jason C
PY  - 2024
DA  - 2024/12/11
TI  - Early Detection of Dementia in Populations With Type 2 Diabetes: Predictive Analytics Using Machine Learning Approach
JO  - J Med Internet Res
SP  - e52107
VL  - 26
KW  - diabetes
KW  - dementia
KW  - machine learning
KW  - prediction model
KW  - TMUCRD
KW  - Taipei Medical University Clinical Research Database
AB  - Background: The possible association between diabetes mellitus and dementia has raised concerns, given the observed coincidental occurrences. Objective: This study aimed to develop a personalized predictive model, using artificial intelligence, to assess the 5-year and 10-year dementia risk among patients with type 2 diabetes mellitus (T2DM) who are prescribed antidiabetic medications. Methods: This retrospective multicenter study used data from the Taipei Medical University Clinical Research Database, which comprises electronic medical records from 3 hospitals in Taiwan. This study applied 8 machine learning algorithms to develop prediction models, including logistic regression, linear discriminant analysis, gradient boosting machine, light gradient boosting machine, AdaBoost, random forest, extreme gradient boosting, and artificial neural network (ANN). These models incorporated a range of variables, encompassing patient characteristics, comorbidities, medication usage, laboratory results, and examination data. Results: This study involved a cohort of 43,068 patients diagnosed with type 2 diabetes mellitus, which accounted for a total of 1,937,692 visits. For model development and validation, 1,300,829 visits were used, while an additional 636,863 visits were reserved for external testing. The area under the curve of the prediction models range from 0.67 for the logistic regression to 0.98 for the ANNs. Based on the external test results, the model built using the ANN algorithm had the best area under the curve (0.97 for 5-year follow-up period and 0.98 for 10-year follow-up period). Based on the best model (ANN), age, gender, triglyceride, hemoglobin A1c, antidiabetic agents, stroke history, and other long-term medications were the most important predictors. Conclusions: We have successfully developed a novel, computer-aided, dementia risk prediction model that can facilitate the clinical diagnosis and management of patients prescribed with antidiabetic medications. However, further investigation is required to assess the model’s feasibility and external validity. 
SN  - 1438-8871
UR  - https://www.jmir.org/2024/1/e52107
UR  - https://doi.org/10.2196/52107
UR  - http://www.ncbi.nlm.nih.gov/pubmed/39434474
DO  - 10.2196/52107
ID  - info:doi/10.2196/52107
ER  -