Comparing the Accuracy and Developed Models for Predicting the Confrontation Naming of the Elderly in South Korea using Weighted Random Forest, Random Forest, and Support Vector Regression

Since dementia patients clearly show the retrogression of linguistic ability from the early stage, evaluating cognitive and language abilities is very important when diagnosing dementia. Among them, naming is an essential item (sub-test) that is always included in the dementia-screening test. This study developed confrontation naming prediction models using support vector regression (SVR), random forest, and weighted random forest for the elderly in the community and identified an algorithm showing the best performance by comparing the accuracy of the models. This study used 485 elderly subjects (248 men and 237 women) living in Seoul and Incheon who were 74 years old or older. Prediction models were developed using SVR, random forest, and weighted random forest algorithms. This study revealed that the root mean squared error of weighted random forests was the lowest when comparing the prediction performance using models based on SVR, random forest, and weighted random forest. Future studies are needed to compare the prediction performance of weighted random forest with other machine learning models by calculating various performance indices such as sensitivity, specificity, and harmonic mean using data from various fields to prove the superior prediction performance of weighted random forest.