Accuracy Prediction on Detection of Breast Cancer Using Machine Learning Classifiers
Aditya Pratap Singh, Shobit Agrawal · 2022 14th International Conference on Computational Intelligence and Communication Networks (CICN) · 2022
Breast Cancer becomes a curse to women, and it increases day by day. So, there is a need for a more reliable and precise method for its detection. In this technical era, the medical field has become more advanced with its integration with computer technology. Its detection accuracy is proven more than humans. As it detects the feature that may remain unnoticed by humans. So, in this proposed work it is studied to detect the type of cancers how it can be detected using machine learning algorithm which algorithm is best for detection of these type of dataset i.e. (malignant and benign) these are further introduced in this work. The used dataset has 569 instances with 30 attributes. Therefore, several machine learning techniques i.e., naïve Bayes, logistics Regression, decision tree, k nearest neighbor (KNN), adaptive boosting (Adaboost) are used to make a comparative analysis of these algorithms on basis of their accuracy. Logistic regression outperforms the other classifiers in terms of confusion matrix parameter.