Open AccessPredicting Disease Activity for Biologic Selection in Rheumatoid Arthritis
Author(s) -
Morio Yamauchi,
Kenji Nakano,
Yoshiya Tanaka,
Keiichi Horio
Publication year - 2020
Publication title -
computer science and information technology ( cs and it )
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5121/csit.2020.101913
Subject(s) - rheumatoid arthritis , medicine , selection (genetic algorithm) , missing data , regression , regression analysis , value (mathematics) , disease , linear regression , statistics , computer science , artificial intelligence , machine learning , mathematics
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.