Taking precautions in advance: a lower level of activities of daily living may be associated with a higher likelihood of memory-related diseases

• Published in: Frontiers in public health 2023, Vol.11, p.1293134-1293134
• Main Authors: He, Jiawei, Wang, Weijie, Wang, Shiwei, Guo, Minhua, Song, Zhenyan, Cheng, Shaowu
• Format: Article
• Language: English
• Subjects: Activities of Daily Living; Algorithms; Barthel Index (BI); Humans; Longitudinal Studies; Machine Learning; machine learning algorithm; memory-related diseases; Retirement; the China health and retirement longitudinal survey
• ISSN: 2296-2565; 2296-2565
• DOI: 10.3389/fpubh.2023.1293134

Memory-related diseases (MDs) pose a significant healthcare challenge globally, and early detection is essential for effective intervention. This study investigates the potential of Activities of Daily Living (ADL) as a clinical diagnostic indicator for MDs. Utilizing data from the 2018 national baseline survey of the China Health and Retirement Longitudinal Study (CHARLS), encompassing 10,062 Chinese individuals aged 45 or older, we assessed ADL using the Barthel Index (BI) and correlated it with the presence of MDs. Statistical analysis, supplemented by machine learning algorithms (Support Vector Machine, Decision Tree, and Logistic Regression), was employed to elucidate the relationship between ADL and MDs. MDs represent a significant public health concern, necessitating early detection and intervention to mitigate their impact on individuals and society. Identifying reliable clinical diagnostic signs for MDs is imperative. ADL have garnered attention as a potential marker. This study aims to rigorously analyze clinical data and validate machine learning algorithms to ascertain if ADL can serve as an indicator of MDs. Data from the 2018 national baseline survey of the China Health and Retirement Longitudinal Study (CHARLS) were employed, encompassing responses from 10,062 Chinese individuals aged 45 or older. ADL was assessed using the BI, while the presence of MDs was determined through health report questions. Statistical analysis was executed using SPSS 25.0, and machine learning algorithms, including Support Vector Machine (SVM), Decision Tree Learning (DT), and Logistic Regression (LR), were implemented using Python 3.10.2. Population characteristics analysis revealed that the average BI score for individuals with MDs was 70.88, significantly lower than the average score of 87.77 in the control group. Pearson's correlation analysis demonstrated a robust negative association (  = -0.188,