Machine learning in medicine: a practical introduction to techniques for data pre-processing, hyperparameter tuning, and model comparison

Background There is growing enthusiasm for the application of machine learning (ML) and artificial intelligence (AI) techniques to clinical research and practice. However, instructions on how to develop robust high-quality ML and AI in medicine are scarce. In this paper, we provide a practical examp...

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Vydané v:BMC medical research methodology Ročník 22; číslo 1; s. 1 - 15
Hlavní autori: Pfob, André, Lu, Sheng-Chieh, Sidey-Gibbons, Chris
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London BioMed Central 01.11.2022
BioMed Central Ltd
Springer Nature B.V
BMC
Predmet:
Age
Algorithms
Analysis
Artificial intelligence
Datasets
Guideline
Health aspects
Health Sciences
Machine learning
Mammography
Medical research
Medicine
Medicine & Public Health
Methods
Missing data
Neural networks
Open source software
Patients
Practice guidelines (Medicine)
Programming languages
Public domain
Public software
Research methodology
Statistical Theory and Methods
Statistics for Life Sciences
Theory of Medicine/Bioethics
Variables
United States
Medicine
Guideline
Artificial intelligence
Machine learning
ISSN:1471-2288, 1471-2288
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Shrnutí:Background There is growing enthusiasm for the application of machine learning (ML) and artificial intelligence (AI) techniques to clinical research and practice. However, instructions on how to develop robust high-quality ML and AI in medicine are scarce. In this paper, we provide a practical example of techniques that facilitate the development of high-quality ML systems including data pre-processing, hyperparameter tuning, and model comparison using open-source software and data. Methods We used open-source software and a publicly available dataset to train and validate multiple ML models to classify breast masses into benign or malignant using mammography image features and patient age. We compared algorithm predictions to the ground truth of histopathologic evaluation. We provide step-by-step instructions with accompanying code lines. Findings Performance of the five algorithms at classifying breast masses as benign or malignant based on mammography image features and patient age was statistically equivalent ( P  > 0.05). Area under the receiver operating characteristics curve (AUROC) for the logistic regression with elastic net penalty was 0.89 (95% CI 0.85 – 0.94), for the Extreme Gradient Boosting Tree 0.88 (95% CI 0.83 – 0.93), for the Multivariate Adaptive Regression Spline algorithm 0.88 (95% CI 0.83 – 0.93), for the Support Vector Machine 0.89 (95% CI 0.84 – 0.93), and for the neural network 0.89 (95% CI 0.84 – 0.93). Interpretation Our paper allows clinicians and medical researchers who are interested in using ML algorithms to understand and recreate the elements of a comprehensive ML analysis. Following our instructions may help to improve model generalizability and reproducibility in medical ML studies.
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ISSN:1471-2288
1471-2288
DOI:10.1186/s12874-022-01758-8