Academy & Industry Research Collaboration Center (AIRCC)

Volume 10, Number 19, December 2020

Predicting Disease Activity for Biologic Selection in Rheumatoid Arthritis


Morio YAMAUCHI1, Kazuhisa NAKANO2, Yoshiya TANAKA2 and Keiichi HORIO1, 1Kyushu Institute of Technology, Japan, 2University of Occupational and Environmental Health, Japan


In this article, we implemented a regression model and conducted experiments for predicting disease activity using data from 1929 rheumatoid arthritis patients to assist in the selection of biologics for rheumatoid arthritis. On modelling, the missing variables in the data were completed by three different methods, mean value, self-organizing map and random value. Experimental results showed that the prediction error of the regression model was large regardless of the missing completion method, making it difficult to predict the prognosis of rheumatoid arthritis patients.


Rheumatoid Arthritis, Gaussian Process Regression, Self-Organizing Map.