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Abstract Recent studies showed that machine learning models such as gradient-boosting decision tree (GBDT) can predict diabetes with high accuracy from big data. In this study, we asked whether highly accurate prediction of diabetes is possible even from small data by expanding the amount of data through data collaboration (DC) analysis, a modern framework for integrating and analyzing data accumulated at multiple institutions while ensuring confidentiality. To this end, we focused on data from two institutions: health checkup data of 1502 citizens accumulated in Tsukuba City and health history data of 1399 patients collected at the University of Tsukuba Hospital. When using only the health checkup data, the ROC-AUC and Recall for logistic regression (LR) were 0.858 ± 0.014 and 0.970 ± 0.019, respectively, while those for GBDT were 0.856 ± 0.014 and 0.983 ± 0.016, respectively. When using also the health history data through DC analysis, these values for LR improved to 0.875 ± 0.013 and 0.993 ± 0.009, respectively, while those for GBDT deteriorated because of the low compatibility with a method used for confidential data sharing (although DC analysis brought improvements). Even in a situation where health checkup data of only 324 citizens are available, the ROC-AUC and Recall for LR were 0.767 ± 0.025 and 0.867 ± 0.04, respectively, thanks to DC analysis, indicating an 11% and 12% improvement. Thus, we concluded that the answer to the above question was “Yes” for LR but “No” for GBDT for the data set tested in this study.

Abstract When asked for numerical estimations, people can respond by stating their estimates (e.g., writing down a number) or indicating a number on a scale. Although these methods are logically the same, such differences may affect the responses to the numerical estimations. In this study, we examined how differences in response format affected responses to numerical estimations using two behavioral experiments. We found that participants showed a round number bias (i.e., people answered estimates with round numbers) when simply stating a number and the distribution of responses tended to be less diverse. In contrast, this tendency was not observed when the participants responded using a scale. Participants provided more diverse estimates when they answered using a scale. Furthermore, we analyzed how this difference in response distribution was related to the wisdom of crowds (the aggregated judgment is as accurate as, or sometimes better than, the best individual judgment in the group) using computer simulations. The results indicated that round number bias affected the achievement of the wisdom of crowds. Particularly, when the group size was small, biased responses resulted in less effective achievement. Our findings suggest that using an appropriate scale is a low-cost method for eliminating round number bias and efficiently achieving the wisdom of crowds.