A Machine Learning Method for Classification of Cervical Cancer

Tanimu, Jesse Jeremiah; Hamada, Mohamed; Hassan, Mohammed; Kakudi, Habeebah Adamu; Abiodun, John Oladunjoye

doi:10.3390/electronics11030463

Cited by 61 publications

(30 citation statements)

References 56 publications

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“…Machine learning methods can be used to process vast amounts of cancer data and is readily accessible to the medical research community to upgrade the survival rate of patients. 19,20 The present study used various ML algorithms to predict indications for various examinations to diagnose cervical cancer. The Fine Gaussian SVM classifier was the best model to classify Hinselmann, cytology and biopsy.…”

Section: Discussionmentioning

confidence: 99%

Optimised feature selection and cervical cancer prediction using Machine learning classification

Tak¹,

Parihar²,

Singh³

et al. 2022

Scripta Medica

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Background: Screening and early detection play a key role in cervical cancer prevention. The present study predicts the outcome of various diagnostic tests used to diagnose cervical cancer using machine learning algorithms. Methods: The present study ran various cervical cancer risk factors on a machine learning (ML) classifier to predict outcomes of Hinselmann, Schiller, cytology and biopsy. The dataset is publicly available on the Machine Learning Repository website of the University of California Irvine. The imbalanced dataset was pre-processed using oversampling methods. The significantly varied features between the two levels of a response variable were used to train the machine learning classifiers on MATLAB. The classifiers used were Decision Trees, Support Vector Machine, K-Nearest Neighbours and Ensemble learning classifiers. The performance metrics of the classifiers were expressed as accuracy, the area under the receiver operator characteristic (AU-ROC) curve, sensitivity and specificity. Results: The Fine Gaussian SVM classifier was the best to classify Hinselmann, cytology and biopsy with the accuracy of 97.5 %, 62.5 % and 98 %, respectively. However, Boosted trees performed best in the classification of Schiller with 81.3 % accuracy. Conclusion: The present study selected optimised features among multiple risk factors to train various ML classifiers to predict cervical cancer.

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Section: Discussionmentioning

confidence: 99%

Optimised feature selection and cervical cancer prediction using Machine learning classification

Tak¹,

Parihar²,

Singh³

et al. 2022

Scripta Medica

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show abstract

“…Over the past decade, researchers have advocated the use of MLP in cervical cancer prediction due to its respectable classification accuracy [ 10 , 18 , 19 , 20 , 25 , 26 , 29 , 30 ]; therefore, MLP is selected for optimization by GA in this study. In addition, various researchers used SVM, RF, LR DT, KNN, NB, LDA, and AdaBoost to classify cervical cancer [ 4 , 9 , 10 , 11 , 13 , 22 , 23 , 30 , 31 , 32 , 33 ]. Those algorithms are among the most common classification algorithms in modeling medical datasets.…”

Section: Methodsmentioning

confidence: 99%

Cervical Cancer Diagnosis Using an Integrated System of Principal Component Analysis, Genetic Algorithm, and Multilayer Perceptron

Dweekat

Lam

2022

Healthcare

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Cervical cancer is one of the most dangerous diseases that affect women worldwide. The diagnosis of cervical cancer is challenging, costly, and time-consuming. Existing literature has focused on traditional machine learning techniques and deep learning to identify and predict cervical cancer. This research proposes an integrated system of Genetic Algorithm (GA), Multilayer Perceptron (MLP), and Principal Component Analysis (PCA) that accurately predicts cervical cancer. GA is used to optimize the MLP hyperparameters, and the MLPs act as simulators within the GA to provide the prediction accuracy of the solutions. The proposed method uses PCA to transform the available factors; the transformed features are subsequently used as inputs to the MLP for model training. To contrast with the PCA method, different subsets of the original factors are selected. The performance of the integrated system of PCA–GA–MLP is compared with nine different classification algorithms. The results indicate that the proposed method outperforms the studied classification algorithms. The PCA–GA–MLP model achieves the best accuracy in diagnosing Hinselmann, Biopsy, and Cytology when compared to existing approaches in the literature that were implemented on the same dataset. This study introduces a robust tool that allows medical teams to predict cervical cancer in its early stage.

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“…Researchers have used a number of methods to impute missing values in datasets that occur due to the incorrect collection of data values. Some of the methods include deletion, mean, imputer method, mode and median [18,32,33]. Deletion causes a great loss of information when missing values are concentrated in a single feature [34].…”

Section: Data Pre-processingmentioning

confidence: 99%

An LDA–SVM Machine Learning Model for Breast Cancer Classification

et al. 2022

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Breast cancer is a prevalent disease that affects mostly women, and early diagnosis will expedite the treatment of this ailment. Recently, machine learning (ML) techniques have been employed in biomedical and informatics to help fight breast cancer. Extracting information from data to support the clinical diagnosis of breast cancer is a tedious and time-consuming task. The use of machine learning and feature extraction techniques has significantly changed the whole process of a breast cancer diagnosis. This research work proposed a machine learning model for the classification of breast cancer. To achieve this, a support vector machine (SVM) was employed for the classification, and linear discriminant analysis (LDA) was employed for feature extraction. We measured our model’s feature extraction performance in principal component analysis (PCA) and random forest for classification. A comparative analysis of the proposed model was performed to show the effectiveness of the feature extraction, and we computed missing values based on the classifier’s accuracy, precision, and recall. The original Wisconsin Breast Cancer dataset (WBCD) and Wisconsin Prognostic Breast Cancer dataset (WPBC) were used. We evaluated performance in two phases: In phase 1, rows containing missing values were computed using the mean, and in phase 2, rows containing missing values were computed using the median. LDA–SVM when median was used to compute missing values has better results, with accuracy of 99.2%, recall of 98.0% and precision of 98.0% on the WBCD dataset and an accuracy of 79.5%, recall of 76.0% and precision of 59.0% on the WPBC dataset. The SVM classifier had a better performance in handling classification problems when LDA was applied and the median was used as a method for computing missing values.

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A Machine Learning Method for Classification of Cervical Cancer

Cited by 61 publications

References 56 publications

Optimised feature selection and cervical cancer prediction using Machine learning classification

Optimised feature selection and cervical cancer prediction using Machine learning classification

Cervical Cancer Diagnosis Using an Integrated System of Principal Component Analysis, Genetic Algorithm, and Multilayer Perceptron

An LDA–SVM Machine Learning Model for Breast Cancer Classification

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