Utilizing Machine Learning to Predict the Malignancy of a Breast Tumor
DOI:
https://doi.org/10.62439/harp-145Keywords:
Machine Learning, Prediction, Tumors, MalignancyAbstract
Machine learning is creating computational models from data that predict outcomes and facilitate decisions. Using past data, machine learning can help predict the label of whether a current patient will more or less likely have breast cancer given only the features. This paper describes the process of creating a model to test if a patient has breast cancer using a dataset with data on the radius, texture, compactness, etc., of 569 breast tumors. One hot encoding, binary classification, validation and training sets, neural networks, binary cross-entropy error, accuracy, and the testing of epochs and different combinations of nodes, hidden layers, and activation functions will be included in this paper.
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Copyright (c) 2024 Megan Hu
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.