Abstract：
BACKGROUND: The prevalence of cognitive impairment and dementia in the older population is increasing, and thereby, early detection of cognitive decline is essential for effective intervention.
METHODS: This study included 2,288 participants with normal cognitive function from the Ma\'anshan Healthy Aging Cohort Study. Forty-two potential predictors, including demographic characteristics, chronic diseases, lifestyle factors, anthropometric indices, physical function, and baseline cognitive function, were selected based on clinical importance and previous research. The dataset was partitioned into training, validation, and test sets in a proportion of 60% for training, 20% for validation, and 20% for testing, respectively. Recursive feature elimination was used for feature selection, followed by six machine learning algorithms that were employed for model development. The performance of the models was evaluated using area under the curve (AUC), specificity, sensitivity, and accuracy. Moreover, SHapley Additive exPlanations (SHAP) was conducted to access the interpretability of the final selected model and to gain insights into the impact of features on the prediction outcomes. SHAP force plots were established to vividly show the application of the prediction model at the individual level.
RESULTS: The final predictive model based on the Naive Bayes algorithm achieved an AUC of 0.820 (95% CI, 0.773-0.887) on the test set, outperforming other algorithms. The top ten influential features in the model included baseline Mini-Mental State Examination (MMSE), education, self-reported economic status, collective or social activities, Pittsburgh sleep quality index (PSQI), body mass index, systolic blood pressure, diastolic blood pressure, instrumental activities of daily living, and age. The model demonstrated the potential to identify individuals at a higher risk of cognitive impairment within 3 years from older adults.
CONCLUSIONS: The predictive model developed in this study contributes to the early detection of cognitive impairment in older adults by primary healthcare staff in community settings.
METHODS: This study included 2,288 participants with normal cognitive function from the Ma\'anshan Healthy Aging Cohort Study. Forty-two potential predictors, including demographic characteristics, chronic diseases, lifestyle factors, anthropometric indices, physical function, and baseline cognitive function, were selected based on clinical importance and previous research. The dataset was partitioned into training, validation, and test sets in a proportion of 60% for training, 20% for validation, and 20% for testing, respectively. Recursive feature elimination was used for feature selection, followed by six machine learning algorithms that were employed for model development. The performance of the models was evaluated using area under the curve (AUC), specificity, sensitivity, and accuracy. Moreover, SHapley Additive exPlanations (SHAP) was conducted to access the interpretability of the final selected model and to gain insights into the impact of features on the prediction outcomes. SHAP force plots were established to vividly show the application of the prediction model at the individual level.
RESULTS: The final predictive model based on the Naive Bayes algorithm achieved an AUC of 0.820 (95% CI, 0.773-0.887) on the test set, outperforming other algorithms. The top ten influential features in the model included baseline Mini-Mental State Examination (MMSE), education, self-reported economic status, collective or social activities, Pittsburgh sleep quality index (PSQI), body mass index, systolic blood pressure, diastolic blood pressure, instrumental activities of daily living, and age. The model demonstrated the potential to identify individuals at a higher risk of cognitive impairment within 3 years from older adults.
CONCLUSIONS: The predictive model developed in this study contributes to the early detection of cognitive impairment in older adults by primary healthcare staff in community settings.
摘要：
早期检测老年人的认知能力下降对于有效干预至关重要。这项研究,马鞍山健康老龄化队列研究的一部分，检查了2288名认知功能正常的参与者。42个潜在的预测因子,包括人口统计，慢性疾病,生活方式因素，和基线认知功能，被选中。数据集分为训练，验证,和测试集(60%，20%,20%,分别)。递归特征消除（RFE）和六种机器学习算法用于模型开发。使用曲线下面积（AUC）评估模型性能，特异性，灵敏度,和准确性。沙普利附加扩张(SHAP)被应用于可解释性，揭示了十大有影响力的特征：基线MMSE，教育,经济地位,社会活动,PSQI,BMI,SBP,DBP,IADL,和年龄。基于朴素贝叶斯(NB)算法的模型在测试集上实现了0.820(95%CI0.773-0.887)的AUC，优于其他算法。该模型可以帮助社区环境中的初级卫生保健人员识别出老年人中三年内患认知障碍风险较高的个体。
