Prediction of conversion to dementia using interpretable machine learning in patients with amnestic mild cognitive impairment

Min Young Chun, Chae Jung Park, Jonghyuk Kim, Jee Hyang Jeong, Hyemin Jang, Kyunga Kim, Sang Won Seo

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


Purpose: Amnestic mild cognitive impairment (aMCI) is a transitional state between normal aging and Alzheimer’s disease (AD). However, not all aMCI patients are observed to convert to AD dementia. Therefore, developing a predictive algorithm for the conversion of aMCI to AD dementia is important. Parametric methods, such as logistic regression, have been developed; however, it is difficult to reflect complex patterns, such as non-linear relationships and interactions between variables. Therefore, this study aimed to improve the predictive power of aMCI patients’ conversion to dementia by using an interpretable machine learning (IML) algorithm and to identify the factors that increase the risk of individual conversion to dementia in each patient. Methods: We prospectively recruited 705 patients with aMCI who had been followed-up for at least 3 years after undergoing baseline neuropsychological tests at the Samsung Medical Center between 2007 and 2019. We used neuropsychological tests and apolipoprotein E (APOE) genotype data to develop a predictive algorithm. The model-building and validation datasets were composed of data of 565 and 140 patients, respectively. For global interpretation, four algorithms (logistic regression, random forest, support vector machine, and extreme gradient boosting) were compared. For local interpretation, individual conditional expectations (ICE) and SHapley Additive exPlanations (SHAP) were used to analyze individual patients. Results: Among the four algorithms, the extreme gradient boost model showed the best performance, with an area under the receiver operating characteristic curve of 0.852 and an accuracy of 0.807. Variables, such as age, education, the scores of visuospatial and memory domains, the sum of boxes of the Clinical Dementia Rating scale, Mini-Mental State Examination, and APOE genotype were important features for creating the algorithm. Through ICE and SHAP analyses, it was also possible to interpret which variables acted as strong factors for each patient. Conclusion: We were able to propose a predictive algorithm for each aMCI individual’s conversion to dementia using the IML technique. This algorithm is expected to be useful in clinical practice and the research field, as it can suggest conversion with high accuracy and identify the degree of influence of risk factors for each patient.

Original languageEnglish
Article number898940
JournalFrontiers in Aging Neuroscience
StatePublished - 5 Aug 2022

Bibliographical note

Funding Information:
This research was supported by a grant of the Korean Health Technology R&D Project, Ministry of Health and Welfare, Republic of Korea (HI19C1132); a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health and Welfare and Ministry of science and ICT, Republic of Korea (grant numbers: HU20C0111 and HU22C0170); the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2019R1A5A2027340); Institute of Information and Communications Technology Planning and Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2021-0-02068, Artificial Intelligence Innovation Hub); Future Medicine 20*30 Project of the Samsung Medical Center (#SMX1220021); and the “National Institute of Health” research project (2021-ER1006-01).

Publisher Copyright:
Copyright © 2022 Chun, Park, Kim, Jeong, Jang, Kim and Seo.


  • Alzheimer’s disease
  • SHapley Additive exPlanations (SHAP)
  • amnestic mild cognitive impairment
  • artificial intelligence
  • clinical decision-support system
  • interpretable machine learning
  • prediction algorithm


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