Identification of the Risk Factors of Cervical Cancer Applying Feature Selection Approaches

Ahmed, Mohiuddin; Kabir, Mir Md. Jahangir; Kabir, Monika; Hasan, Md. Mehedi

doi:10.1109/icecte48615.2019.9303554

Cited by 9 publications

(7 citation statements)

References 4 publications

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“…From 1997—when cervical cancer screening was performed for the first time with AI—until today, various machine learning algorithms have been applied for the detection of cervical cancer [ 30 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 ]. Common machine learning (ML) models included deep learning (DL), k-nearest neighbors (KNN), artificial neural network (ANN), decision tree (DT), random forest (RF), support vector machine (SVM), logistic regression (LR), synthetic minority oversampling technique (SMOTE), convolutional neural network (CNN), multilayer perceptron (MLP), deep neural networks (DNN), the PAPNET test, and ResNet (residual neural network or a combination of techniques) [ 36 , 39 , 45 , 46 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 ,…”

Section: Resultsmentioning

confidence: 99%

“…Multivariate adjustment and multiple-regression techniques were introduced for prediction (that is, for estimating the predicted value of a certain outcome as a function of given values of independent variables) [ 82 ]. AI studies using machine learning principles have focused on algorithms to predict cervical cancer [ 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 ]. The most important predictors of cervical cancer were age, age at first sexual intercourse, number of sexual partners, pregnancies, smoking, period of smoking (years), hormonal contraceptives, period of use of hormonal contraceptives (years), IUD, period of use of IUD (years), STDs, period of STDs (years), Schiller, Hinselmann, cytology, the presence of 15 high-risk HPV genotypes [ 55 , 56 , 57 , 58 , 60 , 84 ], social status, marital status, personal health level, education level, and the number of caesarean deliveries [ 63 ].…”

Section: Resultsmentioning

confidence: 99%

“…AI studies using machine learning principles have focused on algorithms to predict cervical cancer [ 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 ]. The most important predictors of cervical cancer were age, age at first sexual intercourse, number of sexual partners, pregnancies, smoking, period of smoking (years), hormonal contraceptives, period of use of hormonal contraceptives (years), IUD, period of use of IUD (years), STDs, period of STDs (years), Schiller, Hinselmann, cytology, the presence of 15 high-risk HPV genotypes [ 55 , 56 , 57 , 58 , 60 , 84 ], social status, marital status, personal health level, education level, and the number of caesarean deliveries [ 63 ]. However, according to Mudawi et al, certain characteristics of the patient samples, including the quantity of alcohol consumed and the presence of HIV and HSV2, could not be regarded as reliable predictors [ 95 ].…”

Section: Resultsmentioning

confidence: 99%

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Diagnosis of Cervical Cancer and Pre-Cancerous Lesions by Artificial Intelligence: A Systematic Review

et al. 2022

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Objective: The likelihood of timely treatment for cervical cancer increases with timely detection of abnormal cervical cells. Automated methods of detecting abnormal cervical cells were established because manual identification requires skilled pathologists and is time consuming and prone to error. The purpose of this systematic review is to evaluate the diagnostic performance of artificial intelligence (AI) technologies for the prediction, screening, and diagnosis of cervical cancer and pre-cancerous lesions. Materials and Methods: Comprehensive searches were performed on three databases: Medline, Web of Science Core Collection (Indexes = SCI-EXPANDED, SSCI, A & HCI Timespan) and Scopus to find papers published until July 2022. Articles that applied any AI technique for the prediction, screening, and diagnosis of cervical cancer were included in the review. No time restriction was applied. Articles were searched, screened, incorporated, and analyzed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. Results: The primary search yielded 2538 articles. After screening and evaluation of eligibility, 117 studies were incorporated in the review. AI techniques were found to play a significant role in screening systems for pre-cancerous and cancerous cervical lesions. The accuracy of the algorithms in predicting cervical cancer varied from 70% to 100%. AI techniques make a distinction between cancerous and normal Pap smears with 80–100% accuracy. AI is expected to serve as a practical tool for doctors in making accurate clinical diagnoses. The reported sensitivity and specificity of AI in colposcopy for the detection of CIN2+ were 71.9–98.22% and 51.8–96.2%, respectively. Conclusion: The present review highlights the acceptable performance of AI systems in the prediction, screening, or detection of cervical cancer and pre-cancerous lesions, especially when faced with a paucity of specialized centers or medical resources. In combination with human evaluation, AI could serve as a helpful tool in the interpretation of cervical smears or images.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Diagnosis of Cervical Cancer and Pre-Cancerous Lesions by Artificial Intelligence: A Systematic Review

et al. 2022

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“…Other works that use the cervical cancer dataset tend to describe the preprocessing step in more detail, for example, Ahishakiye et al, 70 Ahmed et al, 71 and Ijaz et al 72 The two primary data quality issues are (a) the missing values and (b) the unbalanced class distribution. The most common preprocessing choices for (a) include removing columns with high missing value ratio, removing rows with missing values, and imputation (mostly with the average and the most frequent value).…”

Section: Applicationsmentioning

confidence: 99%

PrAVA: Preprocessing profiling approach for visual analytics

Milani

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Paulovich

et al. 2021

Information Visualization

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To accommodate the demands of a data-driven society, we have expanded our ability to collect and store data, develop sophisticated algorithms, and generate elaborated visual representations of the data analysis process outcomes. However, data preprocessing, as the activity of transforming the raw data into an appropriate format for subsequent analysis, is still a challenging part of this process. Although we can find studies that address the use of visualization techniques to support the activities in the scope of preprocessing, the current Visual Analytics processes do not consider preprocessing an equally important phase in their processes. Hence, with this paper, we aim to contribute to the discussion of how we can incorporate the preprocessing as a prominent phase in the Visual Analytics process and promote better alternatives to assist the data analysts during the preprocessing activities. To achieve that, we are introducing the Preprocessing Profiling Approach for Visual Analytics (PrAVA), a conceptual Visual Analytics process that includes Preprocessing Profiling as a new phase. It also contemplates a set of guidelines to be considered by new solutions adopting PrAVA. Moreover, we analyze its applicability through use case scenarios that show resourceful methods for data understanding and evaluation of the preprocessing impacts. As a final contribution, we indicate a list of research opportunities in the scope of preprocessing combined with visualization and Visual Analytics to stimulate a shift to visual preprocessing.

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“…They explored various types of feature selection techniques, namely RFE, Boruta algorithm, and simulated algorithm (SA) to determine the relevant risk factors for diagnosing cervical cancer. Reference [29] also used RFE to identify the factors that have much impact in the prediction of cervical cancer. SVM, multilayer perceptron, and LR classifiers were applied and performance were evaluated based on accuracy, specificity and AUC score.…”

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confidence: 99%

A Machine Learning Method for Classification of Cervical Cancer

et al. 2022

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Cervical cancer is one of the leading causes of premature mortality among women worldwide and more than 85% of these deaths are in developing countries. There are several risk factors associated with cervical cancer. In this paper, we developed a predictive model for predicting the outcome of patients with cervical cancer, given risk patterns from individual medical records and preliminary screening. This work presents a decision tree (DT) classification algorithm to analyze the risk factors of cervical cancer. Recursive feature elimination (RFE) and least absolute shrinkage and selection operator (LASSO) feature selection techniques were fully explored to determine the most important attributes for cervical cancer prediction. The dataset employed here contains missing values and is highly imbalanced. Therefore, a combination of under and oversampling techniques called SMOTETomek was employed. A comparative analysis of the proposed model has been performed to show the effectiveness of feature selection and class imbalance based on the classifier’s accuracy, sensitivity, and specificity. The DT with the selected features from RFE and SMOTETomek has better results with an accuracy of 98.72% and sensitivity of 100%. DT classifier is shown to have better performance in handling classification problems when the features are reduced, and the problem of high class imbalance is addressed.

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Identification of the Risk Factors of Cervical Cancer Applying Feature Selection Approaches

Cited by 9 publications

References 4 publications

Diagnosis of Cervical Cancer and Pre-Cancerous Lesions by Artificial Intelligence: A Systematic Review

Diagnosis of Cervical Cancer and Pre-Cancerous Lesions by Artificial Intelligence: A Systematic Review

PrAVA: Preprocessing profiling approach for visual analytics

A Machine Learning Method for Classification of Cervical Cancer

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