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Table 1 Results of persistent homology application combined with supervised algorithms with different k-fold cross-validation value for the classification of Gleason score 3 of cancerous prostate gland images

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

  1. The bold signifies the start and the end of the procedure of classification