Predictive Analysis for Prognostication of Breast Cancer

Abstract

AbstractNowadays, most of the women affected by one of the type of cancer is Breast Cancer (BC). The main objective is to identify and prognosis of the Breast Cancer at its earlier stages using supervised machine learning (ML) techniques like Naive Bayes (NB) classifier, support vector machine (SVM), random forest classifier (RFC), K-nearest neighbor (KNN) and decision tree classifier (DT). The data are collected from the Wisconsin Diagnostic Breast Cancer dataset then malignant and benign tumors are classified based on the dataset and using various ML techniques to predict the Breast Cancer in advance manner. Various parameters like accuracy, precision, sensitivity (recall), F-measure (specificity), regression score, variance measure, maximum error and balanced accuracy are measured in each algorithms. Using Python experimental tool PyCharm to execute ML algorithms for predicts the Breast Cancer. Our final result indicates that support vector machine is the best one for predictive Breast Cancer efficiently with accuracy of 99%. KeywordsMachine learning (ML)Breast Cancer (BC)Support vector machine (SVM)K-nearest neighbor (KNN)Random forest classifier (RFC)Decision tree classifier (DT)Naive Bayes (NB)