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Performance Analysis of Machine Learning Classification Algorithms for Breast Cancer Diagnosis

Machine Learning based learning algorithms gives the machine the power to learn on its own without being explicitly programmed by a programmer. It helps in automating the task like classification, clustering, etc. which previously required human intervention. Machine Learning-based classification algorithms today are being widely used for the diagnosis of various diseases such as Breast Cancer. Breast Cancer is one of the primary causes of death among women all over the world in the past recent years. In this paper, Random Forest, Logistics Regression, Decision Tree, Naive Bayes and SVM classification algorithms have been implemented. The experiment in this article has been conducted on the most popular Wisconsin Diagnosis Breast Cancer Dataset (WDBC)[l]. The main objective of conducting this experiment is to analyze the correctness and accuracy of classification algorithms used to classify cancer-causing tumours (Malignant) from non-cancerous tumours (Benign) with the highest precision and accuracy. The experiment results show that Random Forest gave the highest accuracy amongst all the classification algorithms.