Statistical Learning in Medical Research with Decision Threshold and Accuracy Evaluation

Sande, Sumaiya Z.; Seng, Loraine; Li, Jialiang; D’Agostino, Ralph

doi:10.6339/21-JDS1022

Journal of Data Science

Statistical Learning in Medical Research with Decision Threshold and Accuracy Evaluation

Volume 19, Issue 4 (2021), pp. 634–657

Sumaiya Z. Sande Loraine Seng Jialiang Li All authors (4)

https://doi.org/10.6339/21-JDS1022

Pub. online: 23 September 2021 Type: Data Science Reviews

Received
10 May 2021

Accepted
18 August 2021

Published
23 September 2021

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.

Supplementary material

Supplementary Materials

Supplementary material online include: The review of different smoothers used in Generalized additive models, Installation details for R interface for Keras and Tensorflow, data and R code needed to reproduce the results.

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Keywords

deep learning machine learning net benefit ROC threshold

Funding

The work was partly supported by Academic Research Funds R-155-000-205-114, R-155-000-195-114 and Tier 2 MOE funds in Singapore MOE2017-T2-2-082: R-155-000-197-112 (Direct cost) and R-155-000-197-113 (IRC).

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