Data-Driven Diagnosis of Cervical Cancer With Support Vector Machine-Based Approaches

Wu, Wen; Zhou, Hao

doi:10.1109/access.2017.2763984

Cited by 139 publications

(82 citation statements)

References 30 publications

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“…Abdoh et al [10] concluded in their research that the following factors pose the highest risk for the development of this disease: sexually transmitted disease (STDs), intra-uterine device (IUD), hormonal contraceptives and the age at which first sexual intercourse happens. Wu and Zhou [11] claimed that the number of sexual partners, the age when first sexual intercourse happens, the number of smoke packs smoked per year and the number of years that the patient uses hormonal contraceptives increase the possibility of developing cervical cancer. Nithya and Ilango [12] identified ten core features as being most important for predicting cancer.…”

Section: A Cervical Cancer and Risk Factorsmentioning

confidence: 99%

Predicting Cervical Cancer using Machine Learning Methods

Alsmariy¹,

Healy²,

Abdelhafez³

2020

IJACSA

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In almost all countries, precautionary measures are less expensive than medical treatment. The early detection of any disease gives a patient better chances of successful treatment than disease discovery at an advanced stage of its development. If we do not know how to treat patients, any treatment we can provide would be useful and would provide a more comfortable life. Cervical cancer is one such disease, considered to be fourth among the most common types of cancer in women around the world. There are many factors that increase the risk of cervical cancer, such as age and use of hormonal contraceptives. Early detection of cervical cancer helps to raise recovery rates and reduce death rates. This paper aims to use machine learning algorithms to find a model capable of diagnosing cervical cancer with high accuracy and sensitivity. The cervical cancer risk factor dataset from the University of California at Irvine (UCI) was used to construct the classification model through a voting method that combines three classifiers: Decision tree, logistic regression and random forest. The synthetic minority oversampling technique (SMOTE) was used to solve the problem of imbalance dataset and, together with the principal component analysis (PCA) technique, to reduce dimensions that do not affect model accuracy. Then, stratified 10-fold cross-validation technique was used to prevent the overfitting problem. This dataset contains four target variables-Hinselmann, Schiller, Cytology, and Biopsy-with 32 risk factors. We found that using the voting classifier, SMOTE and PCA techniques helped raise the accuracy, sensitivity, and area under the Receiver Operating Characteristic curve (ROC_AUC) of the predictive models created for each of the four target variables to higher rates. In the SMOTE-voting model, accuracy, sensitivity and PPA ratios improved by 0.93 % to 5.13 %, 39.26 % to 46.97 % and 2 % to 29 %, respectively for all target variables. Moreover, using PCA technology reduced computational processing time and increasing model efficiency. Finally, after comparing our results with several previous studies, it was found that our models were able to diagnose cervical cancer more efficiently according to certain evaluation measures.

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Section: A Cervical Cancer and Risk Factorsmentioning

confidence: 99%

Predicting Cervical Cancer using Machine Learning Methods

Alsmariy¹,

Healy²,

Abdelhafez³

2020

IJACSA

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“…The rule number shows 8.5, and the number of terms per rule is 9.7. The classification performance results are compared with the other approaches [31,32] that applied on the same cervical cancer data set without the use of FS techniques, such as SVM, as shown in Table 4. Furthermore, the comparison with the Ant-Miner after using BA for FS shows an increase in the accuracy and decrease in the number of terms rules with a small increase in the number of rules as shown in Figure 2 and Figure 3.…”

Section: Resultsmentioning

confidence: 99%

Hybrid bat-ant colony optimization algorithm for rule-based feature selection in health care

Sagban

Marhoon

Alubady

2020

IJECE

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Rule-based classification in the field of health care using artificial intelligence provides solutions in decision-making problems involving different domains. An important challenge is providing access to good and fast health facilities. Cervical cancer is one of the most frequent causes of death in females. The diagnostic methods for cervical cancer used in health centers are costly and time-consuming. In this paper, bat algorithm for feature selection and ant colony optimization-based classification algorithm were applied on cervical cancer data set obtained from the repository of the University of California, Irvine to analyze the disease based on optimal features. The proposed algorithm outperforms other methods in terms of comprehensibility and obtains better results in terms of classification accuracy.

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“…In a study that compared the C5.0 algorithm with the SVM algorithm by Tseng et al [17] on the dataset of 12 attributes over 168 samples, 118 are chosen to be the training data and 50 as test data. C5.0 algorithm achieved the classification accuracy of 92.44%.…”

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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Data-Driven Diagnosis of Cervical Cancer With Support Vector Machine-Based Approaches

Cited by 139 publications

References 30 publications

Predicting Cervical Cancer using Machine Learning Methods

Predicting Cervical Cancer using Machine Learning Methods

Hybrid bat-ant colony optimization algorithm for rule-based feature selection in health care

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

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