From: Machine learning techniques on homological persistence features for prostate cancer diagnosis
k-fold/method | LDA | NBC | SVM | DTC | RF | |||||
---|---|---|---|---|---|---|---|---|---|---|
Accuracy (%) | AUC | Accuracy (%) | AUC | Accuracy (%) | AUC | Accuracy (%) | AUC | Accuracy (%) | AUC | |
K = 2 fold (\({\raise0.7ex\hbox{$1$} \!\mathord{\left/ {\vphantom {1 2}}\right.\kern-\nulldelimiterspace} \!\lower0.7ex\hbox{$2$}}\) training \({\raise0.7ex\hbox{$1$} \!\mathord{\left/ {\vphantom {1 2}}\right.\kern-\nulldelimiterspace} \!\lower0.7ex\hbox{$2$}}\) testing) | 53.7 | 0.5042 | 52.4 | 0.5523 | 63.5 | 0.7378 | 95.2 | 0.9652 | 65.6 | 0.7830 |
K = 3 fold (\({\raise0.7ex\hbox{$2$} \!\mathord{\left/ {\vphantom {2 3}}\right.\kern-\nulldelimiterspace} \!\lower0.7ex\hbox{$3$}}\) training \({\raise0.7ex\hbox{$1$} \!\mathord{\left/ {\vphantom {1 3}}\right.\kern-\nulldelimiterspace} \!\lower0.7ex\hbox{$3$}}\) testing) | 54.6 | 0.5065 | 53.5 | 0.5762 | 64.2 | 0.7422 | 97.4 | 0.9813 | 65.7 | 0.7831 |
K = 4 fold (\({\raise0.7ex\hbox{$3$} \!\mathord{\left/ {\vphantom {3 4}}\right.\kern-\nulldelimiterspace} \!\lower0.7ex\hbox{$4$}}\) training \({\raise0.7ex\hbox{$1$} \!\mathord{\left/ {\vphantom {1 4}}\right.\kern-\nulldelimiterspace} \!\lower0.7ex\hbox{$4$}}\) testing) | 53.5 | 0.5035 | 54.8 | 0.5728 | 64.8 | 0.7431 | 97.2 | 0.9789 | 65.8 | 0.7855 |
K = 5 fold (\({\raise0.7ex\hbox{$4$} \!\mathord{\left/ {\vphantom {4 5}}\right.\kern-\nulldelimiterspace} \!\lower0.7ex\hbox{$5$}}\) training \({\raise0.7ex\hbox{$1$} \!\mathord{\left/ {\vphantom {1 5}}\right.\kern-\nulldelimiterspace} \!\lower0.7ex\hbox{$5$}}\) testing) | 55.1 | 0.5135 | 56.7 | 0.5823 | 65.7 | 0.7586 | 96.9 | 0.9755 | 65.9 | 0.7863 |