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Related Experiment Video

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Learning Modern Laryngeal Surgery in a Dissection Laboratory

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Evaluating classification accuracy for modern learning approaches.

Jialiang Li^1,2,3, Ming Gao^4,5, Ralph D'Agostino⁶

¹Department of Statistics and Applied Probability, National University of Singapore, Singapore.

Statistics in Medicine

|February 1, 2019

Summary

This summary is machine-generated.

This tutorial provides methods for evaluating classification accuracy in deep learning models like multilayer perceptron (MLP) and convolutional neural network (CNN). It addresses challenges with multicategory variables and offers R code for practical application in biostatistics.

Keywords:

R package convolutional neural net deep learning multilayer perceptron mxnet neural network

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Area of Science:

Artificial Intelligence
Biostatistics
Machine Learning

Background:

Deep learning models, including multilayer perceptron (MLP) and convolutional neural network (CNN), are powerful AI tools.
Practitioners currently lack readily available performance evaluation methods for these advanced techniques.
Traditional accuracy measures are often insufficient for qualitative response variables with multiple categories.

Purpose of the Study:

To provide a tutorial on evaluating classification accuracy for state-of-the-art shallow and deep learning methods.
To review statistical concepts for multicategory classification accuracy and their extensions.
To demonstrate the utility of these concepts with real medical examples and R code.

Main Methods:

Review of statistical concepts for multicategory classification accuracy.
Demonstration of these concepts using real medical data.
Provision of problem-based R code for step-by-step statistical computations.

Main Results:

The tutorial offers practical guidance on assessing the performance of various learning algorithms.
It highlights the applicability of extended accuracy measures for multicategory classification.
Real medical examples illustrate the step-by-step computation process.

Conclusions:

The developed analysis tools aim to enhance the practical application of deep learning in biostatistics.
Broader adoption of these statistical evaluation methods is expected among practitioners.
This work facilitates a more comprehensive understanding of deep learning model performance in medical research.