Fig. 2

Leave-one-out cross-validation accuracy of prediction according to the number of candidate Kampo formulas. The discriminant rate via leave-one-out cross-validation was highest when we tried to predict a Kampo formula from among two candidates, hachimijiogan and kamishoyosan. The discriminant rate decreased as the number of candidate Kampo formulas increased; in particular, it showed a marked drop in shifting from three to four candidates, meaning when we added the fourth Kampo formula keishibukuryogan to the top three formulas, hachimijiogan, kamishoyosan, and keishikaryukotsuboreito (see also Table 2). The non-specialist variable set included age, sex, body mass index, and subjective symptoms, as well as the two essential and predictable traditional medicine pattern diagnoses (excess–deficiency and heat–cold) according to Kampo specialists. The specialist variable set included abdominal examination findings and body constituent patterns in addition to all of the predictor variables of the non-specialist variable set