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Updated: Jul 30, 2026
Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
Published on: July 27, 2018
Machine learning-enabled risk prediction of self-neglect among community-dwelling older adults in China
Teng-Fei Li1, Yuan Xu2, Jian-Wei Li2
1Department of Health Promotion and Behavioural Sciences, School of Public Health, Anhui Medical University, Hefei, China.
Background:
Elder self-neglect (ESN) is usually ignored as a private problem and impairs the health outcomes of older adults. It is essential to construct a robust and efficient tool for risk prediction which can better detect and prevent self-neglect among older adults.
Methods:
This study included 2494 study participants from the Ma'anshan Healthy Ageing Cohort (MHAC). First, the group-based trajectory model (GBTM) was used to estimate ESN development trajectory groups. Then, feature selection methods were used to select variables; after that, we compared six machine learning models (Decision Tree Classifier (DT), K-Nearest Neighbour (KNN), Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM) and XGBoost (XGB)). In addition, Synthetic Minority Oversampling Technique (SMOTE) is used to address the data imbalance problem.
Results:
The results show that the ESN can be defined as two trajectory groups (rising and stable). After feature selection, the final model contains eight predictors. The area under the curve (AUC) of the raw dataset was 0.637-0.769. In the dataset with SMOTE, the AUC was 0.635-0.765 and RF was the optimal model. The top five most important characteristics were quality of life, psychological resilience, social support, education, and income.
Conclusions:
The RF developed in this study may be considered a simple and scientific aid in the risk prediction of self-neglect among community-dwelling old adults.
