Machine Learning Algorithms for Prediction of Breast Cancer Survival
DOI:
https://doi.org/10.59681/2175-4411.v15.iEspecial.2023.1091Keywords:
Survival Analysis, Machine Learning, Breast cancerAbstract
Objective: This paper aims to show a comparative analysis of Machine Learning algorithms applied to Breast Cancer Survival prediction. Methods: Descriptive study that considered data from 1,570 patients with stage I-III breast cancer. The Synthetic Minority Oversampling Technique was applied due to an imbalance in the dataset. The Naive Bayes, Random Forest, Multilayer Perceptron and AdaBoost algorithms were considered in the study, and cross-validation as a learning strategy. Results: The model developed from the Random Forest algorithm showed greater accuracy (96.2%; 95%CI: 95.5%-96.9%) and specificity (97.4%; 95%CI: 96.6%-98.2% ); and the model developed from AdaBoost, greater sensitivity (95.3%; 95%CI: 94.3%-96.4%). Conclusion: Thus, among the models presented in our study, the one developed from the Random Forest algorithm presented, in general, the best evaluation measures in the prediction of breast cancer survival.
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