Machine learning approach for unmet medical needs among middle-aged adults in South Korea: a cross-sectional study

South Korea is reported to have higher levels of unmet medical needs (UMN) than other countries, particularly among the middle-aged adult population. Considering that this group constitutes a substantial portion of the country's productive workforce, their health requires continuous management to ensure sustained productivity. The purpose of this study is to investigate the factors associated with UMN in economically active middle-aged adults and to develop a model to predict the occurrence of UMN.

In this study, 3,575 middle-aged adults who are economically active were selected from the 2020 Korean Health Panel Survey data. Logistic regression, Random Forest, Naïve Bayes, Gradient Boosting Method, and Neural Network were applied to create the prediction model, and tenfold cross validation was performed by checking the reliability of the analysis. The model was evaluated based on the Area Under Receiver Operating Characteristics (AUROC) as well as accuracy, precision, recall, F-1 score and MCC.

First, the prevalence of UMN in middle-aged adults was 15.6%. Second, random forest was found to be the model with the highest predictive power. It showed an AUROC of 0.831, Accuracy of 0.862, and F-1 score of 0.820. Third, the main factors influencing the occurrence of UMN were subjective stress and subjective health awareness.

These findings suggest that psychological support is necessary in order to manage the occurrence of UMN among middle-aged adults, with regular stress management being especially important. However, the lower AUROC suggests that additional variables are needed to enhance the prediction model.