Mining based Optimization for Breast Cancer Analysis: A Review

Kumar, M. Nagendra; Tomar, Shivkumar Singh; Gaur, Bhupesh

doi:10.5120/21124-3402

Cited by 4 publications

(2 citation statements)

References 23 publications

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“…Murari Kumar, Shivkumar Singh and Tomar Bhupesh Gaur [5] The authors have proposed a framework with optimized mining that can be a better way in breast cancer detection. There are several research directions in this field and the scope is in the direction to fulfill it in the earlier stage.…”

Section: Literature Reviewmentioning

confidence: 99%

A Review on Data Mining Techniques for Prediction of Breast Cancer Recurrence

Padmapriya

Vadivu²

2019

SSRN Journal

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The most common type of cancer in women worldwide is the Breast Cancer. Breast cancer may be detected early using Mammograms, probably before it's spread. Recurrent breast cancer could occur months or years after initial treatment. The cancer could return within the same place because the original cancer (local recurrence), or it may spread to different areas of your body (distant recurrence). Early stage treatment is done not only to cure breast cancer however additionally facilitate in preventing its repetition/recurrence. Data mining algorithms provide assistance in predicting the early-stage breast cancer that continually has been difficult analysis drawback. The projected analysis can establish the most effective algorithm that predicts the recurrence of the breast cancer and improve the accuracy the algorithms. Large information like Clump, Classification, Association Rules, Prediction and Neural Networks, Decision Trees can be analyzed using data mining applications and techniques.

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Section: Literature Reviewmentioning

confidence: 99%

A Review on Data Mining Techniques for Prediction of Breast Cancer Recurrence

Padmapriya

Vadivu²

2019

SSRN Journal

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“…The researchers collected the clinical dataset to evaluate the grammatical problem using machine learning tools. Kumar et al showed [9] an idea on the breast cancer dataset which helped to eradicate the early death risk because of their comprehensive research. Different data mining tools were developed to make a prediction system for Breast cancer by A. F. M. Agarap [10].…”

Section: Introductionmentioning

confidence: 99%

Expert cancer model using supervised algorithms with a LASSO selection approach

Ghosh

Karim

Atik

et al. 2021

IJECE

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One of the most critical issues of the mortality rate in the medical field in current times is breast cancer. Nowadays, a large number of men and women is facing cancer-related deaths due to the lack of early diagnosis systems and proper treatment per year. To tackle the issue, various data mining approaches have been analyzed to build an effective model that helps to identify the different stages of deadly cancers. The study successfully proposes an early cancer disease model based on five different supervised algorithms such as logistic regression (henceforth LR), decision tree (henceforth DT), random forest (henceforth RF), Support vector machine (henceforth SVM), and K-nearest neighbor (henceforth KNN). After an appropriate preprocessing of the dataset, least absolute shrinkage and selection operator (LASSO) was used for feature selection (FS) using a 10-fold cross-validation (CV) approach. Employing LASSO with 10-fold cross-validation has been a novel steps introduced in this research. Afterwards, different performance evaluation metrics were measured to show accurate predictions based on the proposed algorithms. The result indicated top accuracy was received from RF classifier, approximately 99.41% with the integration of LASSO. Finally, a comprehensive comparison was carried out on Wisconsin breast cancer (diagnostic) dataset (WBCD) together with some current works containing all features.

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Analysis of Machine Learning Algorithms on Cancer Dataset

Prabadevi

Deepa

et al. 2020

2020 International Conference on Emerging Trends in Information Technology and Engineering (Ic-Etite)

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Mining based Optimization for Breast Cancer Analysis: A Review

Cited by 4 publications

References 23 publications

A Review on Data Mining Techniques for Prediction of Breast Cancer Recurrence

A Review on Data Mining Techniques for Prediction of Breast Cancer Recurrence

Expert cancer model using supervised algorithms with a LASSO selection approach

Analysis of Machine Learning Algorithms on Cancer Dataset

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