School of Integrated Chinese and Western Medicine, Hunan University of Chinese Medicine, Changsha, China.
Key Laboratory of Hunan Province for Integrated Traditional Chinese and Western Medicine on Prevention and Treatment of Cardio-Cerebral Diseases, College of Integrated Traditional Chinese and Western Medicine, Hunan University of Chinese Medicine, Changsha, China.
Front Public Health. 2023 Dec 15;11:1293134. doi: 10.3389/fpubh.2023.1293134. eCollection 2023.
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, < 0.001) between ADL and MDs. After adjusting for covariates such as gender, age, smoking status, drinking status, hypertension, diabetes, and dyslipidemia, the negative relationship between ADL and MDs remained statistically significant ( = -0.002, = -0.142, = -14.393, 95% CI = -0.002, -0.001, = 0.000). The application of machine learning models further confirmed the predictive accuracy of ADL for MDs, with area under the curve (AUC) values as follows: SVM-AUC = 0.69, DT-AUC = 0.715, LR-AUC = 0.7. Comparative analysis of machine learning outcomes with and without the BI underscored the BI's role in enhancing predictive abilities, with the DT model demonstrating superior performance.
This study establishes a robust negative correlation between ADL and MDs through comprehensive statistical analysis and machine learning algorithms. The results validate ADL as a promising diagnostic indicator for MDs, with enhanced predictive accuracy when coupled with the Barthel Index. Lower levels of ADL are associated with an increased likelihood of developing memory-related diseases, underscoring the clinical relevance of ADL assessment in early disease detection.
记忆相关疾病（MDs）在全球范围内构成重大医疗保健挑战，早期检测对于有效干预至关重要。本研究探讨日常生活活动（ADL）作为 MDs 临床诊断指标的潜力。利用 2018 年中国健康与退休纵向研究（CHARLS）的全国基线调查数据，涵盖了 10062 名年龄在 45 岁或以上的中国个体，我们使用巴氏量表（BI）评估 ADL，并将其与 MDs 的存在相关联。采用统计分析，辅以机器学习算法（支持向量机、决策树和逻辑回归），阐明 ADL 与 MDs 之间的关系。
MDs 是一个重大的公共卫生问题，需要早期检测和干预，以减轻其对个人和社会的影响。确定 MDs 的可靠临床诊断标志至关重要。ADL 已引起关注，成为一种潜在的标志物。本研究旨在通过严格的临床数据分析和验证机器学习算法，确定 ADL 是否可以作为 MDs 的指标。
使用 2018 年中国健康与退休纵向研究（CHARLS）的全国基线调查数据，涵盖了 10062 名年龄在 45 岁或以上的中国个体。使用 BI 评估 ADL，通过健康报告问题确定 MDs 的存在。使用 SPSS 25.0 进行统计分析，使用 Python 3.10.2 实现机器学习算法，包括支持向量机（SVM）、决策树学习（DT）和逻辑回归（LR）。
人口特征分析显示，患有 MDs 的个体的平均 BI 得分为 70.88，明显低于对照组的平均得分 87.77。Pearson 相关分析显示 ADL 与 MDs 之间存在强负相关（r=−0.188，p<0.001）。在调整性别、年龄、吸烟状况、饮酒状况、高血压、糖尿病和血脂异常等协变量后，ADL 与 MDs 之间的负相关关系仍然具有统计学意义（r=−0.002，p=−0.142，p=−14.393，95%CI=−0.002，−0.001，p=0.000）。机器学习模型的应用进一步证实了 ADL 对 MDs 的预测准确性，曲线下面积（AUC）值如下：SVM-AUC=0.69，DT-AUC=0.715，LR-AUC=0.7。有和没有 BI 的机器学习结果的比较分析强调了 BI 在增强预测能力方面的作用，DT 模型表现出更好的性能。
本研究通过全面的统计分析和机器学习算法，确立了 ADL 与 MDs 之间的稳健负相关关系。结果验证了 ADL 作为 MDs 的有前途的诊断指标，当与巴氏量表结合使用时，预测准确性得到提高。较低的 ADL 水平与记忆相关疾病的发生几率增加相关，这突出了 ADL 评估在早期疾病检测中的临床相关性。
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