Machine Learning-Based Prediction of Neurodegenerative Disease in Patients With Type 2 Diabetes by Derivation and Validation in 2 Independent Korean Cohorts :Model Development and Validation Study
Background: Several machine learning (ML) prediction models for neurodegenerative diseases (NDs) in type 2 diabetes mellitus(T2DM) have recently been developed. However, the predictive power of these models is limited by the lack of multiple riskfactors. Objective: This study aimed to assess the val...
| Autores: | , , , , , , , , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Fundació Sant Joan de Déu |
| Repositorio: | r-FSJD. Repositorio Institucional de Producción Científica de la Fundació Sant Joan de Déu |
| OAI Identifier: | oai:fsjd.fundanetsuite.com:p26854 |
| Acceso en línea: | https://fsjd.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=26854 |
| Access Level: | acceso abierto |
| Palabra clave: | machine learning neurodegenerative disease diabetes mellitus prediction AdaBoost |
| Sumario: | Background: Several machine learning (ML) prediction models for neurodegenerative diseases (NDs) in type 2 diabetes mellitus(T2DM) have recently been developed. However, the predictive power of these models is limited by the lack of multiple riskfactors. Objective: This study aimed to assess the validity and use of an ML model for predicting the 3-year incidence of ND in patientswith T2DM. Methods: We used data from 2 independent cohorts-the discovery cohort (1 hospital; n=22,311) and the validation cohort (2hospitals; n=2915)-to predict ND. The outcome of interest was the presence or absence of ND at 3 years. We selected differentML-based models with hyperparameter tuning in the discovery cohort and conducted an area under the receiver operatingcharacteristic curve (AUROC) analysis in the validation cohort. Results: The study dataset included 22,311 (discovery) and 2915 (validation) patients with T2DM recruited between 2008 and2022. ND was observed in 133 (0.6%) and 15 patients (0.5%) in the discovery and validation cohorts, respectively. The Ada Boostmodel had a mean AUROC of 0.82 (95% CI 0.79-0.85) in the discovery dataset. When this result was applied to the validationdataset, the AdaBoost model exhibited the best performance among the models, with an AUROC of 0.83 (accuracy of 78.6%,sensitivity of 78.6%, specificity of 78.6%, and balanced accuracy of 78.6%). The most influential factors in the AdaBoost modelwere age and cardiovascular disease. Conclusions: This study shows the use and feasibility of ML for assessing the incidence of ND in patients with T2DM andsuggests its potential for use in screening patients. Further international studies are required to validate these findings. |
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