Cervical Cancer Risk Factors: Classification and Mining Associations

Islam, Arif- Ul-; Ripon, Shamim; Bhuiyan, Nuruddin Qaisar

doi:10.34306/csit.v4i1.85

Cited by 3 publications

(2 citation statements)

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“…According to the results obtained from the study, the RT method classified the biopsy and cytology classes well, while the RF method correctly classified the hinselmann and schiller classes (Ali et al, 2021). In the study of Islam et al (2019), training was carried out using the Decision Tree (DT), RF, Logistic Model Tree (LMT), and Artificial Neural Network (ANN) methods over the cervical cancer clinical dataset. As a result of the experimental evaluation in this study, the RF method correctly classified the biopsy and hinselmann classes, the LMT method Schiller and DT, and the cytology classes (Islam et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…In the study of Islam et al (2019), training was carried out using the Decision Tree (DT), RF, Logistic Model Tree (LMT), and Artificial Neural Network (ANN) methods over the cervical cancer clinical dataset. As a result of the experimental evaluation in this study, the RF method correctly classified the biopsy and hinselmann classes, the LMT method Schiller and DT, and the cytology classes (Islam et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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Cervical Cancer Prediction Using SMOTE Algorithm and Machine Learning Approaches

Kılıçarslan

Gogebakan

Közkurt

2023

Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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Cervical cancer is one of the most successful types of treatment when diagnosed early. In this study, it is aimed to find and classify the disease with data mining methods on the digitized data set obtained as a result of the pap-smear test. Two-stage architecture has been proposed for the diagnosis of cervical cancer. In the first stage of the study, missing data were extracted from the used dataset, and in the second stage, a new dataset was obtained by using the Synthetic Minority Oversampling Technique (SMOTE) algorithm to balance the target classes in the dataset. By applying the majority voting (MV) method to the dataset used in the study, the structure with 4 target variables was reduced to a single target variable. On two data sets, Artificial Neural Network (ANN), Support Vector Machines (SVM), Decision Trees (DT), Random Forest (RF), and K-Nearest Neighbors (KNN) algorithms from data mining methods were used for the diagnosis of cervical cancer. The results obtained from the original dataset and the dataset produced with Smote were compared. ANN is the best method evaluated according to classification success and F-score, and the major voted target variable in the balanced data group produced with the Smote algorithm gave the most successful result. The experimental results showed that the use of MV and SMOTE algorithms together increased the classification success from 93% to 99%.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%