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Breast Cancer Survival Prediction Using Machine Learning

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Computational Intelligence in Oncology

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1016))

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Abstract

Breast cancer is one of the most prevalent cancers in women, and recent breakthroughs in data mining have provided more insight into the disease and its prognosis. We offer a set of machine learning models for predicting breast cancer survival in this chapter. There are 272 occurrences, 1564 characteristics, and a target variable in the original data, which were obtained from the data world Website (195 not survived, 77 survived). Out of 1564 features, the top ten features were selected using the extreme gradient boosting (XGB) method. 5-fold and 10-fold stratified cross-validation were used to extract the average results and accuracy for both methods is compared. The outcomes of six machine learning classifiers were compared and rated using a variety of statistical rates (accuracy, precision, true positive rate, true negative rate, F1-score, ROC–AUC score). We offer random forest (RFC) and XGB as the top classifiers after evaluating the models, with overall testing accuracy of 78% and 77.2%, respectively. However, all the classifiers performed well in predicting label 0 (high true negative rate) as compared to label 1 (low true positive rate).

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Authors and Affiliations

  1. Centre for Advanced Computational Solutions (C-fACS), Lincoln University, Lincoln, New Zealand

    Parul Tiwari & Piyush Bhardwaj

  2. Department of Mechanical, Electronics and Chemical Engineering, Oslo Metropolitan University, Oslo, Norway

    Arvind Keprate

  3. Department of Forensic Medicine and Toxicology, SHKM Govt. Medical College, Nalhar, Haryana, India

    Ashish Tyagi

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  1. Parul Tiwari
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  2. Piyush Bhardwaj
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  3. Arvind Keprate
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  4. Ashish Tyagi
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Editors and Affiliations

  1. Department of Computer Science, Jamia Millia Islamia, New Delhi, India

    Khalid Raza

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Tiwari, P., Bhardwaj, P., Keprate, A., Tyagi, A. (2022). Breast Cancer Survival Prediction Using Machine Learning. In: Raza, K. (eds) Computational Intelligence in Oncology. Studies in Computational Intelligence, vol 1016. Springer, Singapore. https://doi.org/10.1007/978-981-16-9221-5_8

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