Breast cancer with machine learning in MATLAB
Brooke E. Sanders · ThinkTech (Texas Tech University) · 2019
We will look at a case study done in Wisconsin of breast cancer data and analyze various variables in order to determine whether breast tissue is malignant or benign. Through analyzing the various variables, we will use Classification Learner, Regression Leaner, and Neural Net Clustering in MATLAB, in order to determine which methods, offer the highest degree of accuracy in determining our original data. As you will see in the Classification Learner the Quadratic SVM and the Cubic SVM with thirty predictors will classify a breast cancer mass as malignant or benign with a 98.2% accuracy. And the Fine Tree algorithm with thirty predictors can classify a breast cancer mass as malignant or benign with a 93.1% accuracy. So, clearly Quadratic SVM and the Cubic SVM with thirty predictors are the better algorithms for classifying our data. And after analyzing our data in the Regression Learner depending on our response our algorithm will vary but we will still be able to determine which algorithm offers a better fit depending on model statics. And in the Neural Net Clustering app our data will be represented by just thirteen neurons due to the correlation that exists amongst our variables in our SOM Neural Net.