Assessment for the Model Predicting of the Cognitive and Language Ability in the Mild Dementia by the Method of Data-Mining Technique

Assessments of cognitive and verbal functions are widely used as screening tests to detect early dementia. This study developed an early dementia prediction model for Korean elderly based on random forest algorithm and compared its results and precision with those of logistic regression model and decision tree model. Subjects of the study were 418 elderly (135 males and 283 females) over the age of 60 in local communities. Outcome was defined as having dementia and explanatory variables included digit span forward, digit span backward, confrontational naming, Rey Complex Figure Test (RCFT) copy score, RCFT immediate recall, RCFT delayed recall, RCFT recognition true positive, RCFT recognition false positive, Seoul Verbal Learning Test (SVLT) immediate recall, SVLT delayed recall, SVLT recognition true positive, SVLT recognition false positive, Korean Color Word Stroop Test (K-CWST) color reading correct, and K-CWST color reading error. The Random Forests algorithm was used to develop prediction model and the result was compared with logistic regression model and decision tree based on chi-square automatic interaction detector (CHAID). As the result of the study, the tests with high level of predictive power in the detection of early dementia were verbal memory, visuospatial memory, naming, visuospatial functions, and executive functions. In addition, the random forests model was more accurate than logistic regression and CHIAD. In order to effectively detect early dementia, development of screening test programs is required which are composed of tests with high predictive power. Keywords—random forests; data mining; mild dementia; risk factors; neuropsychological test