Machine Learning Models for Breast Cancer Classification

Abstract Objectives To develop a machine learning technique that could enable the automatic classification of malignancies of the breast either as benign or malignant, using clinical and pathological data extracted from diagnostic images and patient records. Method It has clinical data and tumor characteristics features, such as clump thickness, uniformity of cell size, and marginal adhesion. In this work, several machine learning algorithms are going to be implemented, such as Decision Trees, Support Vector Machines, Naive Bayes, and K-Nearest Neighbors. Data preprocessing steps include handling missing values and feature standardization. The performance metric used in evaluating these models are Accuracy, Precision, Recall, the F1-score, the Receiver Operating characteristic graph, and Confusion Matrices. Findings: It has been observed in the experiment's outcomes that the accuracy rating was 97.14%, thus making the SVM model better than the Decision Trees, Naive Bayes, and KNN. The models were plus for the SVM model concerning precision, recall, and F1-score; hence, it was the most effective classifier in detecting benign and malignant tumors. Novelty: The paper has provided an overall comparison of different machine learning methods applied earlier in breast cancer classification and strongly administers the effectiveness of SVM. Having multiple algorithms at one's disposal, detailed metrics on performance add to great worth in helping health providers to make proper and informed decisions about diagnosis and management regarding breast cancer.