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
Por:
Sang H, Lee H, Park J, Kim S, Woo HG, Koyanagi A, Smith L, Lee S, Hwang YC, Park TS, Lim H, Yon DK and Rhee SY
Publicada:
3 oct 2024
Ahead of Print:
3 oct 2024
Resumen:
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.
Filiaciones:
Sang H:
Department of Endocrinology and Metabolism, Kyung Hee University Medical Center, Kyung Hee University College of Medicine, Seoul, Republic of Korea
Center for Digital Health, Medical Science Research Institute, Kyung Hee University Medical Center, Kyung Hee University College of Medicine, Seoul, Republic of Korea
Lee H:
Center for Digital Health, Medical Science Research Institute, Kyung Hee University Medical Center, Kyung Hee University College of Medicine, Seoul, Republic of Korea
Department of Regulatory Science, Kyung Hee University, Seoul, Republic of Korea
Park J:
Center for Digital Health, Medical Science Research Institute, Kyung Hee University Medical Center, Kyung Hee University College of Medicine, Seoul, Republic of Korea
Department of Regulatory Science, Kyung Hee University, Seoul, Republic of Korea
Kim S:
Department of Family Medicine, Kyung Hee University Medical Center, Kyung Hee University College of Medicine, Seoul, Republic of Korea
Woo HG:
Department of Neurology, Kyung Hee University Medical Center, Kyung Hee University College of Medicine, Seoul, Republic of Korea
Koyanagi A:
Research and Development Unit, Parc Sanitari Sant Joan de Déu, Barcelona, Spain
Smith L:
Centre for Health, Performance and Wellbeing, Anglia Ruskin University, Cambridge, United Kingdom
Lee S:
Department of Internal Medicine, Gachon University College of Medicine, Incheon, Republic of Korea
Hwang YC:
Division of Endocrinology and Metabolism, Department of Internal Medicine, Kyung Hee University Hospital at Gangdong and Kyung Hee University School of Medicine, Seoul, Republic of Korea
Park TS:
Division of Endocrinology and Metabolism, Department of Internal Medicine, Research Institute of Clinical Medicine, Jeonbuk National University, Jeonbuk National University Medical School, Jeonju, Republic of Korea
Lim H:
Department of Medical Nutrition, Graduate School of East-West Medical Science, Kyung Hee University, Yongin, Republic of Korea
Yon DK:
Center for Digital Health, Medical Science Research Institute, Kyung Hee University Medical Center, Kyung Hee University College of Medicine, Seoul, Republic of Korea
Department of Regulatory Science, Kyung Hee University, Seoul, Republic of Korea
Department of Precision Medicine, Kyung Hee University College of Medicine, Seoul, Republic of Korea
Department of Pediatrics, Kyung Hee University College of Medicine, Seoul, Republic of Korea
Rhee SY:
Department of Endocrinology and Metabolism, Kyung Hee University Medical Center, Kyung Hee University College of Medicine, Seoul, Republic of Korea
Center for Digital Health, Medical Science Research Institute, Kyung Hee University Medical Center, Kyung Hee University College of Medicine, Seoul, Republic of Korea
Department of Regulatory Science, Kyung Hee University, Seoul, Republic of Korea
Department of Precision Medicine, Kyung Hee University College of Medicine, Seoul, Republic of Korea
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