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Prediction of Breast Cancer Recurrence in Five Years using Machine Learning Techniques and SHAP

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Intelligent Computing Techniques for Smart Energy Systems

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 862))

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Abstract

Cancer recurrence is the leading concern among the patients who are affected by breast cancer. Earlier detection of cancer relapse can help the clinicians in administering the right dose of treatment for the right duration of time. This can improve the prognosis of the patient and also reduce their fear regarding relapse. For this purpose, our work deals with using various machine learning (ML) models such as logistic regression, support vector machine (SVM), decision tree, and random forest to predict if a patient will have cancer relapse in 5 years. The performance of all the ML models is then compared by making use of some evaluation metrics such as C-index, accuracy, F1-score, precision, and recall. Random forest (RF) model produced the best performance when compared to all the other models. It gave a precision value of 0.75, accuracy of 0.69, recall of 0.66, C-index of 0.71, and F1-score of 0.70. Random forest model interpretation was done by using Shapley additive explanations (SHAP). This algorithm identifies the contribution given by each feature in cancer recurrence prediction by random forest model. Based on the SHAP values, the features which had high impact on model prediction were found. Features such as tumor size, mutation count, lymph nodes examined positive, age at diagnosis, Nottingham prognostic index (NPI), tumor stage, HER2 status, and cancer type detailed when they had higher values were responsible for causing cancer recurrence in 5 years according to random forest model predictions. Thus, prediction of cancer relapse helps the clinicians in administering the right treatment and improves patient prognosis.

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Authors and Affiliations

  1. Department of Biomedical Engineering, SRM Institute of Science and Technology, Chennai, India

    I. Keren Evangeline & S. P. Angeline Kirubha

  2. Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Chennai, India

    J. Glory Precious

Authors
  1. I. Keren Evangeline
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  2. S. P. Angeline Kirubha
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  3. J. Glory Precious
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Corresponding author

Correspondence to S. P. Angeline Kirubha .

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Editors and Affiliations

  1. Nanyang Technological University, Singapore, Singapore

    Anshuman Tripathi

  2. Manipal University Jaipur, Jaipur, India

    Amit Soni

  3. Manipal University Jaipur, Jaipur, India

    Ashish Shrivastava

  4. Malaviya National Institute of Technology Jaipur, Jaipur, India

    Anil Swarnkar

  5. National Institute of Technology Uttarakhand, Srinagar, India

    Jagrati Sahariya

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Keren Evangeline, I., Angeline Kirubha, S.P., Glory Precious, J. (2022). Prediction of Breast Cancer Recurrence in Five Years using Machine Learning Techniques and SHAP. In: Tripathi, A., Soni, A., Shrivastava, A., Swarnkar, A., Sahariya, J. (eds) Intelligent Computing Techniques for Smart Energy Systems. Lecture Notes in Electrical Engineering, vol 862. Springer, Singapore. https://doi.org/10.1007/978-981-19-0252-9_40

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  • DOI: https://doi.org/10.1007/978-981-19-0252-9_40

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-0251-2

  • Online ISBN: 978-981-19-0252-9

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