Depression Prediction amongst Chinese Older Adults with Neurodegenerative Diseases: A Performance Comparison between Decision Tree Model and Logistic Regression Analysis

TLDR: Wang et al. as mentioned in this paper developed a prediction model using decision tree model to identify factors associated with depression and compare the prediction performance of decision tree with that of logistic regression analysis, which had a much better performance in depression prediction.

Abstract: \n Data analyses using artificial intelligence (AI) have not gained popularity in social work as much as other disciplines. To demonstrate its use, this study focused on Chinese older adults with neurodegenerative diseases (NDs) to (i) develop a prediction model using decision tree model to identify factors associated with depression and (ii) compare the prediction performance of decision tree model with that of logistic regression analysis. Decision tree model processing involved four stages: data collection, data preparation, model development, and result evaluation. An algorithm named Classification and Regression Trees (CARTs) was utilised to grow the decision tree by Python 3.7.1. The performance evaluation was based on accuracy, sensitivity, specificity and Goodness index (G). Seven factors grew the decision tree, including Instrumental Activities of Daily Living (IADLs), Mini-Mental State Examination (MMSE), Health status, Activity of Daily Living (ADL), Gender, Self-rated health change and Age. When compared to logistic regression, the decision tree model had a much better performance in depression prediction. Researchers, practitioners and policymakers need to focus on ways to decrease the vulnerability of depression in Chinese older adults with NDs. Also, the decision tree model can be applied as a referral to other physical or mental diseases prediction and analysis.