Abstract
Due to its high mortality rate and limited survival duration, early identification of breast cancer is vital. The primary application of machine learning models has been the prediction and early diagnosis of breast cancer. This article presents machine learning models and algorithms for automated breast cancer diagnosis using an exploratory approach. The technique makes use of eight distinct machine learning algorithms, including Gaussian Naive Bayes, Logistic Regression, Decision Tree, Random Forest, Support Vector Classifier, XG-Boost, and Deep Learning Model. Their precision is used to evaluate the performance of algorithms and models. Additionally, probabilistic predictions have been made using activation functions like Relu. In terms of test accuracy on the Kaggle data set, Deep Learning Model 2 and the Support Vector Classifier fared the best.
Published Version
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