Abstract
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.
Highlights
Breast cancer is a significant public health concern
According to data released by the World Cancer Observatory in 2018, 52,783 new cancer cases are reported in Morocco each year, with women accounting for 36.9% of these cases [1], The key events linked to poor survival in breast cancer patients are disease progression and metastasis
We compared the difference between the precision results found in the test and the total, this comparison is based on the indicators Accuracy, Precision, sensitivity, specificity, Roc curve and area under the curve (AUC), to measure the performance of these algorithms based on the Confusion Matrix entries
Summary
Breast cancer is a significant public health concern. Due to the development of metastases and uncontrolled growth, various cases of female patients do not respond to therapeutic compounds in breast cancer in the same way [4]. Over the past two decades, personalized medicine has been defined in several ways. More broadly as a predictive, personalized, preventive and participatory health model (“P4 medicine”) [5], and which applies technologies to personalize and deliver care [6]. The use of personalized medicine or precision medicine in oncology aims to adapt treatments according to the characteristics of patients and their diseases by integrating all the biological and genetic, environmental, phenotypic and psychosocial knowledge found there clean [7]. Personalized medicine's ultimate goal is to provide the appropriate treatment to the appropriate person at the appropriate time [8]
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