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अमूर्त

Methods for Improving the Predictive Accuracy Using Multiple Linear Regression Analysis to Predict the Improvement Degree of Functional Independence Measure for Stroke Patients

Makoto Tokunaga, Yoichiro Hashimoto, Susumu Watanabe, Ryoji Nakanishi, Hiroaki Yamanaga, Koichiro Yonemitsu and Hiroyuki Yonemitsu

Multiple linear regression analysis is frequently used in studies investigating the degree of Functional Independence Measure (FIM) improvement in stroke patients. However, the coefficient of determination R2 is about 0.46 to 0.73, meaning that the prediction accuracy is not necessarily high. In order to improve the prediction accuracy, the following methods are used; using appropriate explanatory variables, using FIM effectiveness which corrected the ceiling effect as the objective variable, creating multiple prediction formulas, converting numerical variable of explanatory variables into dummy variable, adding FIM improvement for one month to the explanatory variables. Even so, it is difficult to predict patients whose FIM gain is extremely large or small. It is desirable to combine these methods or develop new methods to achieve the accurate prediction.