Computational Prediction of Cervical Cancer Diagnosis Using Ensemble-Based Classification Algorithm

Gupta, Surbhi; Gupta, Manoj Kumar

doi:10.1093/comjnl/bxaa198

Cited by 53 publications

(17 citation statements)

References 38 publications

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“…This study disclosed that preprocessing methods can improve the cancer prediction outcomes. In 2021, [ 46 ] constructed an ensemble learning model to predict cervical malignancy. The dataset used in the study was derived from the UCI repository.…”

Section: Literature Surveymentioning

confidence: 99%

Prediction Performance of Deep Learning for Colon Cancer Survival Prediction on SEER Data

Gupta

Kalaivani

Rajasundaram

et al. 2022

BioMed Research International

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Colon and rectal cancers are the most common kinds of cancer globally. Colon cancer is more prevalent in men than in women. Early detection increases the likelihood of survival, and treatment significantly increases the likelihood of eradicating the disease. The Surveillance, Epidemiology, and End Results (SEER) programme is an excellent source of domestic cancer statistics. SEER includes nearly 30% of the United States population, covering various races and geographic locations. The data are made public via the SEER website when a SEER limited-use data agreement form is submitted and approved. We investigate data from the SEER programme, specifically colon cancer statistics, in this study. Our objective is to create reliable colon cancer survival and conditional survival prediction algorithms. In this study, we have presented an overview of cancer diagnosis methods and the treatments used to cure cancer. This paper presents an analysis of prediction performance of multiple deep learning approaches. The performance of multiple deep learning models is thoroughly examined to discover which algorithm surpasses the others, followed by an investigation of the network’s prediction accuracy. The simulation outcomes indicate that automated prediction models can predict colon cancer patient survival. Deep autoencoders displayed the best performance outcomes attaining 97% accuracy and 95% area under curve-receiver operating characteristic (AUC-ROC).

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Section: Literature Surveymentioning

confidence: 99%

Prediction Performance of Deep Learning for Colon Cancer Survival Prediction on SEER Data

Gupta

Kalaivani

Rajasundaram

et al. 2022

BioMed Research International

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“…From the presented distributions, it can be noticed that the data set is severely imbalanced. Due to the challenges of imbalanced data classification [42][43][44], several data set balancing techniques will be applied.…”

Section: Data Set Descriptionmentioning

confidence: 99%

Cervical Cancer Diagnostics Using Machine Learning Algorithms and Class Balancing Techniques

et al. 2023

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Objectives:Cervical cancer is present in most cases of squamous cell carcinoma. In most cases, it is the result of an infection with human papillomavirus or adenocarcinoma. This type of cancer is the third most common cancer of the female reproductive organs. The risk groups for cervical cancer are mostly younger women who frequently change partners, have early sexual intercourse, are infected with human papillomavirus (HPV), and who are nicotine addicts. In most cases, the cancer is asymptomatic until it has progressed to the later stages. Cervical cancer screening rates are low, especially in developing countries and in some minority groups. Due to these facts, the introduction of a tentative cervical cancer screening based on a questionnaire can enable more diagnoses of cervical cancer in the initial stages of the disease. Methods: In this research, publicly available cervical cancer data collected on 859 female patients are used. Each sample consists of 36 input attributes and four different outputs Hinselmann, Schiller, cytology, and biopsy. Due to the significant unbalance of the data set, class balancing techniques were used, and these are the Synthetic Minority Oversampling Technique, the ADAptive SYNthetic algorithm (ADASYN), SMOTEEN, random oversampling, and SMOTETOMEK. To obtain the mentioned target outputs, multiple artificial intelligence (AI) and machine learning (ML) methods are proposed. In this research, multiple classification algorithms such as logistic regression, multilayer perceptron (MLP), support vector machine (SVM), K-nearest neighbors (KNN), and several naive Bayes methods were used. Results: From the achieved results, it can be seen that the highest performances were achieved if MLP and KNN are used in combination with Random oversampling, SMOTEEN, and SMOTETOMEK. Such an approach has resulted in mean area under the receiver operating characteristic curve (AUC¯) and mean Matthew’s correlation coefficient (MCC¯) scores of higher than 0.95, regardless of which diagnostic method was used for output vector construction. Conclusions: According to the presented results, it can be concluded that there is a possibility for the utilization of artificial intelligence (AI) and machine learning (ML) techniques for the development of a tentative cervical cancer screening method, which is based on a questionnaire and an AI-based algorithm. Furthermore, it can be concluded that by using class balancing techniques, a certain performance boost can be achieved.

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“…Artificial intelligence (AI) is slowly transforming medical practice ( Gupta and Gupta, 2021a , Gupta and Gupta, 2021b , Gupta and Gupta, 2021c , Gupta and Gupta, 2021d , Gupta and Gupta, 2021e ). AI applications are moving into domains that were previously regarded solely as the domain of human expertise because of recent advances in digital data collecting, machine learning, and computer infrastructure.…”

Section: Artificial Intelligencementioning

confidence: 99%

Artificial intelligence and IoT based prediction of Covid-19 using chest X-ray images

2022

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Computational Prediction of Cervical Cancer Diagnosis Using Ensemble-Based Classification Algorithm

Cited by 53 publications

References 38 publications

Prediction Performance of Deep Learning for Colon Cancer Survival Prediction on SEER Data

Prediction Performance of Deep Learning for Colon Cancer Survival Prediction on SEER Data

Cervical Cancer Diagnostics Using Machine Learning Algorithms and Class Balancing Techniques

Artificial intelligence and IoT based prediction of Covid-19 using chest X-ray images

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