Analysis of Breast Cancer Prediction Using Multiple Machine Learning Methodologies
Keywords:
Breast cancer, machine learning, early detection, Quality Assurance Validation methods, K-Nearest Neighbors (KNN), SVM, Naïve Bayes, Random Forest, Decision TreeAbstract
Breast cancer has the highest fatality rate of any kind of cancer. Cancer screenings should start earlier these days. Several Machine Learning strategies are available for analysing breast cancer data for diagnosis purposes. In this research, a Machine Learning model is provided with the goal of improving breast cancer diagnosis efficiency. Disease prediction accuracy was evaluated using a variety of classifiers, including a random forest, naive bayes, decision tree, support vector machine, and k-nearest neighbours classifier. The software was put through its paces on a breast cancer detection dataset. Accuracy, recall, F1 score, and precision are used to evaluate the system's performance.
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