Statistical Learning in Medical Research with Decision Threshold and Accuracy Evaluation

Abstract

Machine learning methods are increasingly applied for medical data analysis to reduce human efforts and improve our understanding of disease propagation. When the data is complicated and unstructured, shallow learning methods may not be suitable or feasible. Deep learning neural networks like multilayer perceptron (MLP) and convolutional neural network (CNN), have been incorporated in medical diagnosis and prognosis for better health care practice. For a binary outcome, these learning methods directly output predicted probabilities for patient’s health condition. Investigators still need to consider appropriate decision threshold to split the predicted probabilities into positive and negative regions. We review methods to select the cut-off values, including the relatively automatic methods based on optimization of the ROC curve criteria and also the utility-based methods with a net benefit curve. In particular, decision curve analysis (DCA) is now acknowledged in medical studies as a good complement to the ROC analysis for the purpose of decision making. In this paper, we provide the R code to illustrate how to perform the statistical learning methods, select decision threshold to yield the binary prediction and evaluate the accuracy of the resulting classification. This article will help medical decision makers to understand different classification methods and use them in real world scenario.