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

BMC Medical Research Methodology

卷:22

Research

André Pfob¹  Sheng-Chieh Lu²  Chris Sidey-Gibbons² 

[1] Department of Obstetrics and Gynecology, University Breast Unit, Heidelberg University Hospital, Heidelberg, Germany;MD Anderson Center for INSPiRED Cancer Care, The University of Texas MD Anderson Cancer Center, Houston, USA;

[2] MD Anderson Center for INSPiRED Cancer Care, The University of Texas MD Anderson Cancer Center, Houston, USA;Section of Patient-Centered Analytics, The University of Texas MD Anderson Cancer Center, 77030, Houston, TX, USA;

关键词: Machine learning;    Artificial intelligence;    Guideline;    Medicine;   

DOI  :  10.1186/s12874-022-01758-8

 received in 2022-07-19, accepted in 2022-10-18,  发布年份 2022

来源: Springer

【 摘 要 】

BackgroundThere 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.MethodsWe 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.FindingsPerformance 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).InterpretationOur 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.

【 授权许可】

CC BY   
© The Author(s) 2022

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