Comparison of machine learning algorithms for breast cancer

Machine learning algorithms are computer programs that try to predict cancer type based on the past data. The eventual goal of Machine learning algorithms in cancer diagnosis is to have a trained machine learning algorithm that gives the gene expression levels from cancer patient, can accurately predict what type and severity of cancer they have, aiding the doctor in treating it. The existing technology compares three different machine learning algorithms are Decision Tree, Support Vector Machine, Bayesian Belief Network. The main drawback of these algorithms is unusual because the number of features (gene expressions) far exceeds the number of cases (samples taken from patients). Performance efficiency can be achieved by comparing two more algorithms are Random Forest and Naïve Bayes algorithms. Because Random forest and Naïve Bayes are used as feature selection method, Random Forest is used to rank the feature importance and applied for relevant feedback. The requirements are weka tool, Java and Relational Database.

1. Introduction

[1] Machine learning is a branch of artificial intelligence that incorporate a variety of statistical, probabilistic and optimization techniques that allow computers to “learn” from past examples and to detect hardto-diagnosed patterns from massive, noisy or complex data sets. This features are particularly well-suited to medical applications, especially those that depend on complex proteomic and genomic measurements. Machine learning techniques like support vector machine, Bayesian belief network, artificial neural network are frequently used in cancer diagnosis and detection. More recently machine learning has been applied to cancer prognosis and prediction. The survey has shown that there are lots of best performing algorithms for the analysis of features of data sets.