Machine learning-based screening for outpatients with dementia using drawing features from the clock drawing test

Background and Objectives: In geriatrics and dementia care, early diagnosis is crucial. We developed a dementia screening model using drawing features from clock drawing tests (CDT) and investigated the features contributing to the discrimination of dementia and its screening performance. Methods: This study included 129 older adults attending a dementia outpatient clinic. We obtained information on the diagnosis of dementia and CDT data from medical records and quantified 12 types of drawing features according to the Freedman scoring system. Based on the dementia diagnosis information, participants were assigned to two groups: 58 in the dementia diagnosis group and 71 in the non-diagnosis group. Using Boruta, an iterative feature selection algorithm, and a support vector machine, a machine learning method, we analyzed the drawing features contributing to dementia discrimination and evaluated discrimination performance. Results: Five types of drawing features were selected as contributors to discrimination, including "numbers in the correct position," "minute target number indicated," and "hand in correct proportion." These features exhibited a discriminating sensitivity of 0.74 ± 0.16 and specificity of 0.74 ± 0.18 for detecting dementia. Conclusion: This study demonstrated a method for identifying individuals likely to be diagnosed with dementia among patients attending a dementia outpatient clinic using drawing features. The knowledge of drawing features contributing to dementia differentiation may assist healthcare practitioners in clinical reasoning and provide novel insights for clinical practice. In the future, we plan to develop a primary screening for dementia based on machine learning using CDT.