Prediction of Metastatic Relapse in Breast Cancer using Machine Learning Classifiers

The volume and amount of data in cancerology is continuously increasing, yet the vast majority of this data is not being used to uncover useful and hidden insights. As a result, one of the key goals of physicians for therapeutic decision-making during multidisciplinary consultation meetings is to combine prediction tools based on data and best practices (MCM). The current study looked into using CRISP-DM machine learning algorithms to predict metastatic recurrence in patients with early-stage (non-metastatic) breast cancer so that treatment-appropriate medicine may be given to lower the likelihood of metastatic relapse. From 2014 to 2021, data from patients with localized breast cancer were collected at the Regional Oncology Center in Meknes, Morocco. There were 449 records in the dataset, 13 predictor variables and one outcome variable. To create predictive models, we used machine learning techniques such as Support Vector Machine (SVM), Nave Bayes (NB), K-Nearest Neighbors (KNN) and Logistic Regression (LR). The main objective of this article is to compare the performance of these four algorithms on our data in terms of sensitivity, specificity and precision. According to our results, the accuracies of SVM, kNN, LR and NB are 0.906, 0.861, 0.806 and 0.517 respectively. With the fewest errors and maximum accuracy, the SVM classification model predicts metastatic breast cancer relapse. The unbiased prediction accuracy of each model is assessed using a 10-fold cross-validation method.

@article{Merouane2022,
  title     = {Prediction of Metastatic Relapse in Breast Cancer using Machine Learning Classifiers},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {2},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Ertel Merouane and Amali Said and El Faddouli Nour-eddine},
  doi       = {10.14569/IJACSA.2022.0130222},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130222}
}