INTRODUCTION: Dementia is significant issue in aging populations. Treatment efficacy is limited; however, early diagnosis can delay or prevent disease progression. Previous machine learning-based prediction models have limitations (e.g., they are based on clinical parameters or are not generalizable). In this study, prediction models were developed for current and future all-cause dementia.
METHODS: Demographic, socioeconomic, and health-related variables collected from the Korean Longitudinal Study of Aging (KLoSA) were used to develop machine learning-based prediction models for current and future dementia with various algorithms. Cross-validation and two sampling methods were used.
RESULTS: In the initial no-follow-up dataset, 92 of 6,898 participants exhibited dementia. Among 6,207 participants without dementia initially, 69 developed dementia within 2 years. Linear support vector machine (SVM) and radial bias function SVM exhibited the best sensitivity for current and future dementia (79.4% and 77.7%, respectively).
CONCLUSION: We achieved reasonably accurate prediction results for dementia using only non-clinical features .