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Table 3 Extracted characteristics of the included articles

From: Cervical cancer survival prediction by machine learning algorithms: a systematic review

Author, Year

Country

Data source

# Samples

Hyperparameter tuning

Pre-processing

Feature selection

Survival

Data types

ML algorithms

Validation

Evaluation

Liang, 2022 [17]

China

SEER (2010 to 2015)

14946

No

Yes

Yes

OS

Clinical

LR

Internal

C-index

Ding, 2021 [20]

China

The cancer Genome Atlas

542

No

Yes

Yes

OS

Molecular Clinical

SVM

Internal

AUC

Obrzut, 2017 [27]

Poland

Rzeszow State Hospital (1998 to 2001)

117

No

Yes

No

OS

Clinical

PNN

MLP

GEP

SVM

RBFNN

K-Means

Internal

Accuracy Sensitivity

Specificity

AUC

Carlini, 2022 [26]

Italy

IRCCS University Hospital

85

Yes

Yes

Yes

OS

PET/CT

RF

Internal

C-index

Ferreira, 2021 [24]

Belgium

Liege University Hospital (2010 to 2016)

158

Yes

Yes

Yes

DFS

PET/CT Clinical

RF

SVM

NB

LR

External

AUC

F1-score

Precision

Sensitivity

Takada, 2020 [16]

Japan

Chiba Hospital (2012 to 2016)

107

No

Yes

Yes

DFS

Clinical MRI

RF

Internal

AUC

Senthilkumar, 2021 [18]

India

GEO

300

Yes

Yes

Yes

DFS

Molecular

EL

CoxLasso

External

Precision

F1-score

Accuracy

Sensitivity

Shen, 2019 [22]

Taiwan

(2009 to 2015)

142

No

Yes

Yes

DFS

Clinical PET/CT

DL

Internal

Sensitivity

Specificity

Accuracy

PPV

NPV

Arezzo, 2021 [25]

Italy

University of Bari, (2010 to 2018)

92

Yes

No

Yes

PFS

Clinical MRI

LR

RF

KNN

Internal

Accuracy

TPR

Precision

AUC

Guo, 2021 [21]

China

multi-center (2006 to 2017)

5112

Yes

No

Yes

OS; DFS

Clinical

GBDT

RF

Internal

C-index

MAE

Chen, 2022 [23]

China

Nanfang Hospital (2009–2016)

251

No

Yes

Yes

OS; DFS

Clinical WSI

DL

Internal

C-index

AUC

Matsuo, 2019 [28]

USA

California Medical Center (2000 to 2014)

768

Yes

No

Yes

OS; PFS

Clinical

CoxBoost

CoxLasso

RF

DL

Internal

C-index

MAE

Kim, 2021 [19]

Korea

Multi-center (2000 to 2018)

1056

No

Yes

Yes

OS; PFS

Clinical

LR

HL

Internal

AUC

  1. PNN Probabilistic neural network, ANN Artificial neural network, MLP Multilayer perceptron network, GEP Gene expression programming classifier, SVM Support vector machines, RBFNN Radial basis function neural network, RF Random Forest, LR Logistic regression, NB Naïve bayes, DL Deep learning, KNN K-nearest neighbors, DVH Dose-volume Histogram, OS, Overall survival, DFS Disease-free survival, PFS progression-free survival, WSI Whole slide image, EL Ensemble learning, HL Hybrid learning