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

Figure 3

Performance of kernel and type compositions. SVM algorithm is based on its kernel (“l,” “p,” “r,” “s”) and type (“C,” “one,” “eps,” “nu”). Drugs were tested by the 4 × 4 kernel-type cross compositions. In number type, “r-nu” (“rbf” and “nu-regression”) displayed highest score in (b) and (c) and were still in the top class in (a) and (d). In factor type, “p-C” displayed highest scores in (e)–(g) but lower scores in (h). Comprehensively, “p-C” (the best in factor type) performed weaker than “r-nu” (number type). (a) Accuracy (log Y). (b) F1 score (log Y). (c) Kappa (log Y). (d) Sensitivity (log Y). (e) Accuracy (log Y). (f) F1 score (log Y). (g) Kappa (log Y). (h) Sensitivity (log Y).

(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)