Comparative study on data mining classification methods for cervical cancer prediction using pap smear results

Kurniawati, Yulia Ery; Permanasari, Adhistya Erna; Fauziati, Silmi

doi:10.1109/ibiomed.2016.7869827

Cited by 27 publications

(14 citation statements)

References 9 publications

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“…In one of the studies by Wu et al [18], Principal Component Analysis (PCA) for dimensionality reduction and the SVM algorithm for classification were used in a dataset with 32 attributes over 858 samples along with target class variables. These methods resulted in the classification accuracy of 90.48%.…”

Section: Literature Studymentioning

confidence: 99%

A Heuristic and ANN based Classification Model for Early Screening of Cervical Cancer

Priya¹,

Karthikeyan²

2020

IJCIS

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Cervical cancer is one of the most leading causes of mortality among women worldwide. This deadly disease could be prevented by vaccines and easily cured if detected at an early stage. Various researchers focus on providing methods for unambiguous results of screening tests for early diagnosis of cervical cancer and also on detecting stages of cervical cancer through Pap smear images of the cervix. Various socioeconomic factors of women in underdeveloped countries limit the regular Pap smear test for screening of cervical cancer. It is pragmatic that the prediction on the likelihood of cervical cancer is not always possible based on the fewer inquiries from the patients and the data remain inadequate. Oversampling of the data is needed to any dataset for preprocessing the data and this is achieved by using Synthetic Minority Oversampling Technique (SMOTE). In the proposed work, chi-square, a filter-based feature selection method is used to select the attributes based on their correlation between feature and the class to remove the irrelevant attributes from the dataset. Further genetic-based feature selection is used to filter the best optimal features from the selected attributes. Linear Support Vector Machine (SVM) classifier is applied to the selected attributes from the genetic algorithm to aid in predicting the model through training and testing, resulting in an accuracy of 93.82%. Backpropagation, a deep learning method is used as a classification model for cervical cancer, resulting in an improved accuracy of 97.25%. The experimental results show the efficiency of the proposed model is better in comparison to the previous models in terms of accuracy.

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Section: Literature Studymentioning

confidence: 99%

A Heuristic and ANN based Classification Model for Early Screening of Cervical Cancer

Priya¹,

Karthikeyan²

2020

IJCIS

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“…So, for getting the accurate result, we balance our dataset using resample filter of WEKA tool. (Table 1) descrives numerical description of attributes of the dataset [8].…”

Section: Preprocessingmentioning

confidence: 99%

Cervical Cancer Risk Factors: Classification and Mining Associations

Islam¹,

Ripon²,

Bhuiyan³

2020

csit

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Women of all over the world suffer from a common cancer, named Cervical cancer. Cervical cancer cellsgrow slowly at the cervix. This cancer can be avoided if it is recognized and handled in its first stage. Now it is a keychallenge for Medical experts to identify such cancer before it develops extremely. Nowadays, data mining modelsare popularly used to extract hidden patterns from huge medical dataset. This paper introduces data miningtechinques for classification and finding associations in order to detect Cervical cancer at early stage. Afterpreprocessing, the dataset was tested on Decision Tree, Random Forest, Logistic Model Tree and Artificial NeuralNetwork. These methods achieve considerable success in case of both K-fold cross validations and randomly splitdataset. Association rules has been established for detecting comparatively riskier factors which are moreresponsible for cancer development. The proposed methodology can help Medical experts to conduct their researchon Cervical cancer.

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“…Given its prolonged pre-invasive period, accessibility of the infected organ for sampling and the opportunity to administer Pap smear, this cancer appears preventable and diagnosable in early stages [ 8 ]. Moreover, the cytological factors in Pap-smear that are considered as prognostic risk factors for cervical cancer include the shape of gland cells, squamous epithelial tissue, the presence of metaplastic cells, abnormal polymorphic cells and dysplasia cells, different epithelial shapes and the presence of blood, bacteria and fungi in the patients sample [ 9 ]. Research suggests that merely 5% of women in developing countries participate in Pap smear screening programs [ 10 ] and mainly use surgery or radiotherapy to treat this cancer, which exerts different harmful effects on women’s reproductive organs [ 11 - 13 ].…”

Section: Introductionmentioning

confidence: 99%

“…Using non-invasive methods such as supervised machine learning (ML), classification algorithms are crucial for predicting cervical cancer. These models include artificial neural networks [ 19 - 22 ], decision trees [ 9 , 23 - 26 ] and support vector machine (SVM) [ 9 , 23 , 27 - 30 ]. Neural networks are highly-complex analytical techniques that predict new observations from other observations after running the so-called process of “learning” from available data [ 31 ].…”

Section: Introductionmentioning

confidence: 99%

Supervised Algorithms of Machine Learning for the Prediction of Cervical Cancer

2020

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Background: Compared to other genital cancers, cervical cancer is the most prevalent and the main cause of mortality in females in third-world countries, affected by different factors, including smoking, poor nutritional status, immune-deficiency, long-term use of contraceptives and so on. Objective: The present study was conducted to predict cervical cancer and identify its important predictors using machine learning classification algorithms. Material and Methods: In a cross-sectional study, the data of 145 patients with 23 attributes, which referred to Shohada Hospital Tehran, Iran during 2017–2018, were analyzed by machine learning classification algorithms which included SVM, QUEST, C&R tree, MLP and RBF. The criteria measurement used to evaluate these algorithms included accuracy, sensitivity, specificity and area under the curve (AUC). Results: The accuracy, sensitivity, specificity and AUC of Quest and C&R tree were, respectively 95.55, 90.48, 100, and 95.20, 95.55, 90.48, 100, and 95.20, those of RBF 95.45, 90.00, 100 and 91.50, those of SVM 93.33, 90.48, 95.83 and 95.80 and those of MLP 90.90, 90.00, 91.67 and 91.50 percentage. The important predictors in all the algorithms were found to comprise personal health level, marital status, social status, the dose of contraceptives used, level of education and number of caesarean deliveries. Conclusion: This investigation confirmed that ML can enhance the prediction of cervical cancer. The results of this study showed that Decision Tree algorithms can be applied to identify the most relevant predictors. Moreover, it seems that improving personal health and socio-cultural level of patients can be causing cervical cancer prevention.

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Comparative study on data mining classification methods for cervical cancer prediction using pap smear results

Cited by 27 publications

References 9 publications

A Heuristic and ANN based Classification Model for Early Screening of Cervical Cancer

A Heuristic and ANN based Classification Model for Early Screening of Cervical Cancer

Cervical Cancer Risk Factors: Classification and Mining Associations

Supervised Algorithms of Machine Learning for the Prediction of Cervical Cancer

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