Breast cancer diagnosis using machine learning techniques

Abstract

The goal of this research is to diagnose breast cancer using machine learning methods including decision trees, support vector machines (SVM), and naïve Bayesian classifiers. The properties of cell nuclei taken from breast biopsies are included in the Breast Cancer Wisconsin dataset, which is used in this study. Three different machine learning algorithms -naive Bayesian classifier, SVM and decision tree -are used to develop predictive models aimed at classifying samples as malignant or benign tumours. The study involves training the models on evaluation data and then evaluating their performance on test data. In order to compare the effectiveness of each method, many metrics like accuracy, sensitivity, and specificity are used in the evaluation of the results. The outcomes demonstrate the efficacy of every technique examined in this research. This work can be used as a springboard for future research into refining and customizing these techniques to intricate clinical situations, in addition to offering a useful comparison of the efficacy of three distinct machine learning approaches for breast cancer diagnosis. The results may facilitate the application of machine learning techniques in clinical settings, hence facilitating early detection and bettering the prognosis of patients with breast cancer.