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A Machine Learning–Based Predictive Model to Identify Patients Who Failed to Attend a Follow-up Visit for Diabetes Care After Recommendations From a National Screening Program

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posted on 05.04.2022, 13:24 by Akira Okada, Yohei Hashimoto, Tadahiro Goto, Satoko Yamaguchi, Sachiko Ono, Kayo Ikeda Kurakawa, Masaomi Nangaku, Toshimasa Yamauchi, Hideo Yasunaga, Takashi Kadowaki

Reportedly, two-thirds of the patients who were positive for diabetes during screening failed to attend a follow-up visit for diabetes care in Japan. We aimed to develop a machine learning model for predicting people’s failure to attend a follow-up visit.


We conducted a retrospective cohort study of adults with newly screened diabetes at a national screening program using a large Japanese insurance claims database (JMDC, Tokyo, Japan). We defined failure to attend a follow-up visit for diabetes care as no physician consultation during the six months after the screening. The candidate predictors were patient demographics, comorbidities, and medication history. In the training set (randomly selected 80% of the sample), we developed two models (previously reported logistic regression model and Lasso regression model). In the test set (remaining 20%), prediction performance was examined.


We identified 10,645 patients, including 5,450 patients who failed to attend follow-up visits for diabetes care. The Lasso regression model using four predictors had a better discrimination ability than the previously reported logistic regression model using 13 predictors (C-statistic: 0.71 [95% confidence interval, 0.69–0.73] vs. 0.67 [0.65–0.69]; p <0.001). The four selected predictors in the Lasso regression model were lower frequency of physician visits in the previous year, lower HbA1c levels, and negative history of anti-dyslipidemic or anti-hypertensive treatment.


The developed machine learning model using four predictors had a good predictive ability to identify patients who failed to attend a follow-up visit for diabetes care after a screening program.


This work was supported by grants from the Ministry of Health, Labour and Welfare, Japan (21AA2007) and the Ministry of Education, Culture, Sports, Science and Technology, Japan (20K18957, 20H03907, and 21H03159). This work was also supported by a junior scientist development grant from the Japan Diabetes Society to A.O.