Machine Learning for Predicting Hyperglycemic Cases Induced by PD-1/PD-L1 Inhibitors

Figure 2

Performance for data version and algorithm classification. In raw data: f_raw: factor type; n_raw: number type. In complete data: nn: number type, number optimization; ft: factor type, R-tune optimization; fn: factor type, number optimization. To compare the effect of missing values, raw data and complete data (filtered from raw data) were both checked in the pilot test. The positive cases were marked in the form of factor type (“yes” and “no”) or number type (“1” and “0”); the optimization methods were number and tune. According to SVM model options, there were 5 compositing types. The assessing indexes of tested drugs were checked for the more optimized algorithm. All four indexes in raw data displayed lower scores, while these in algorithms (fn, nn) of complete data displayed highest scores. (a) Accuracy. (b) F1 score. (c) Kappa. (d) Sensitivity.

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