Machine learning model for predicting the risk of dementia in patients with depression
10.13929/j.issn.1003-3289.2024.09.007
- VernacularTitle:基于机器学习模型预测抑郁症患者痴呆风险
- Author:
Xuan XIAO
1
,
2
;
Xijian DAI
;
Yihui LI
;
Pei YANG
;
Lianggeng GONG
Author Information
1. 南昌大学第二附属医院医学影像中心,江西南昌 330006
2. 智能医学影像江西省重点实验室,江西南昌 330006
- Keywords:
dementia;
depressive disorder;
machine learning
- From:
Chinese Journal of Medical Imaging Technology
2024;40(9):1309-1313
- CountryChina
- Language:Chinese
-
Abstract:
Objective To observe the value of machine learning model for predicting the risk of dementia in patients with depression.Methods Totally 31 587 depression patients from UK Biobank database were retrospectively enrolled and divided into dementia group(n=896)or non-dementia group(n=30 691)based on follow-up data showed developed dementia or not,also divided into training set(n=18 952)or test set(n=12 635)at the ratio of 6:4.Based on interviews and questionnaire surveys,a total of 190 factors including demographic characteristics,lifestyle,health status,physical indicators and imaging data were included and screened to establish models with light gradient boosting machine(LightGBM),ridge regression(Ridge)and adaptive boosting(AdaBoost),and the value for predicting the risk of dementia in patients with depression was observed.Results A total of 10 factors were ultimately enrolled,including age,waist circumference,employment status,daytime rest,daytime doze or drowsiness,duration of mobile phone use,number of family members,duration of depression,guilt and seeking medical attention due to psychological issues.Based on the above factors,the models were established.In training set,the area under the curve(AUC)of LightGBM,Ridge and AdaBoost model for predicting dementia risk in patients with depression was 0.914,0.832 and 0.889,respectively,and the differences between each 2 models were significant(all P＜0.05);while in test set,the AUC was 0.866,0.842 and 0.859,respectively,except for LightGBM and AdaBoost,the other with significant differences between each two(both P＜0.05).The calibration curve showed that the LightGBM model had the best fit.Conclusion LightGBM machine learning model was helpful for predicting the risk of dementia in patients with depression.
