Prediction using hierarchical data: Applications for automated detection of cervical cancer

Yamal, José‐Miguel; Guillaud, Martial; Atkinson, E. Neely; Follen, Michele; MacAulay, Calum; Cantor, Scott B.; Cox, Dennis D.

doi:10.1002/sam.11261

Cited by 14 publications

(5 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, by using the nested structure of its data to extract patient-level features from the cell-level data, utilizing a statistical model that takes advantage of the hierarchical data structure, and classifying the cellular level [37], it executed comparative research on three primary methods for solving problems. With an estimated 61% sensitivity and 89% specificity on independent data, the optimal method was to classify at the cellular level and count the number of cells with a posterior probability larger than a threshold value.…”

Section: -2015mentioning

confidence: 99%

Cervical Cancer Detection Techniques: A Chronological Review

et al. 2023

View full text Add to dashboard Cite

Cervical cancer is known as a major health problem globally, with high mortality as well as incidence rates. Over the years, there have been significant advancements in cervical cancer detection techniques, leading to improved accuracy, sensitivity, and specificity. This article provides a chronological review of cervical cancer detection techniques, from the traditional Pap smear test to the latest computer-aided detection (CAD) systems. The traditional method for cervical cancer screening is the Pap smear test. It consists of examining cervical cells under a microscope for abnormalities. However, this method is subjective and may miss precancerous lesions, leading to false negatives and a delayed diagnosis. Therefore, a growing interest has been in shown developing CAD methods to enhance cervical cancer screening. However, the effectiveness and reliability of CAD systems are still being evaluated. A systematic review of the literature was performed using the Scopus database to identify relevant studies on cervical cancer detection techniques published between 1996 and 2022. The search terms used included “(cervix OR cervical) AND (cancer OR tumor) AND (detect* OR diagnosis)”. Studies were included if they reported on the development or evaluation of cervical cancer detection techniques, including traditional methods and CAD systems. The results of the review showed that CAD technology for cervical cancer detection has come a long way since it was introduced in the 1990s. Early CAD systems utilized image processing and pattern recognition techniques to analyze digital images of cervical cells, with limited success due to low sensitivity and specificity. In the early 2000s, machine learning (ML) algorithms were introduced to the CAD field for cervical cancer detection, allowing for more accurate and automated analysis of digital images of cervical cells. ML-based CAD systems have shown promise in several studies, with improved sensitivity and specificity reported compared to traditional screening methods. In summary, this chronological review of cervical cancer detection techniques highlights the significant advancements made in this field over the past few decades. ML-based CAD systems have shown promise for improving the accuracy and sensitivity of cervical cancer detection. The Hybrid Intelligent System for Cervical Cancer Diagnosis (HISCCD) and the Automated Cervical Screening System (ACSS) are two of the most promising CAD systems. Still, deeper validation and research are required before being broadly accepted. Continued innovation and collaboration in this field may help enhance cervical cancer detection as well as ultimately reduce the disease’s burden on women worldwide.

show abstract

Section: -2015mentioning

confidence: 99%

Cervical Cancer Detection Techniques: A Chronological Review

et al. 2023

View full text Add to dashboard Cite

show abstract

“…One of the studies by Ho et al [15] used a logistic regression model and compared the results with Chi-square Automatic Interaction Detector (CHAID) model on the dataset with 12 attributes and 710 samples. Initially, logistic regression is built in an assumption that it takes a major position in predicting or classifying the clinical outcomes.…”

Section: Literature Studymentioning

confidence: 99%

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

Priya¹,

Karthikeyan²

2020

IJCIS

View full text Add to dashboard Cite

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.

show abstract

“…The methods and techniques have been used for www.ijacsa.thesai.org screening of cervical cancer are limited to small number of parameters. The available literature for screening of cervical cancer explores mainly Papanicolaou (Pap) smear test [17], hormonal status, FIGO stage [18] and cervical intraepithelial neoplasia (CIN) [19] but only single parameter was used for screening prediction of cervical cancer. The available data mining techniques using large number of parameters [20][21][22][23] were not given effective results.…”

Section: Introductionmentioning

confidence: 99%

Cervical Cancer Prediction through Different Screening Methods using Data Mining

Alam¹,

Milhan²,

Atif³

et al. 2019

ijacsa

View full text Add to dashboard Cite

Cervical cancer remains an important reason of deaths worldwide because effective access to cervical screening methods is a big challenge. Data mining techniques including decision tree algorithms are used in biomedical research for predictive analysis. The imbalanced dataset was obtained from the dataset archive belongs to the University of California, Irvine. Synthetic Minority Oversampling Technique (SMOTE) has been used to balance the dataset in which the number of instances has increased. The dataset consists of patient age, number of pregnancies, contraceptives usage, smoking patterns and chronological records of sexually transmitted diseases (STDs). Microsoft azure machine learning tool was used for simulation of results. This paper mainly focuses on cervical cancer prediction through different screening methods using data mining techniques like Boosted decision tree, decision forest and decision jungle algorithms as well performance evaluation has done on the basis of AUROC (Area under Receiver operating characteristic) curve, accuracy, specificity and sensitivity. 10-fold cross-validation method was utilized to authenticate the results and Boosted decision tree has given the best results. Boosted decision tree provided very high prediction with 0.978 on AUROC curve while Hinslemann screening method has used. The results obtained by other classifiers were significantly worse than boosted decision tree.

show abstract

Prediction using hierarchical data: Applications for automated detection of cervical cancer

Cited by 14 publications

References 37 publications

Cervical Cancer Detection Techniques: A Chronological Review

Cervical Cancer Detection Techniques: A Chronological Review

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

Cervical Cancer Prediction through Different Screening Methods using Data Mining

Contact Info

Product

Resources

About