A Comparative Study of Machine Learning Algorithms applied to Predictive Breast Cancer Data

Diagnostic errors are the most frequent non-operative medical errors. Diagnosis should be more data-driven than trial-and-error. Machine Learning provides techniques for classification and regression purposes which can be used for solving diagnostic problems in different medical domains. Predictive analysis of fatal ailments like cancer using existing data can serve as a diagnosis tool for doctors. The paper aims at a comparative study of Machine Learning algorithms on a predictive breast cancer dataset. The algorithms used for comparison - Artificial Neural Networks (ANN), k-Nearest Neighbors (kNN) and Bayesian Network Classifiers are supervised learning algorithms used widely for classification purposes and are chosen for their diversity. Based on analysis of this data, Artificial Neural Networks are better at classification with 97.4 % accuracy than kNN and Bayesian Classifiers.