Model selection for metabolomics: predicting diagnosis of coronary artery disease using automated machine learning

Abstract

Motivation Selecting the optimal machine learning (ML) model for a given dataset is often challenging. Automated ML (AutoML) has emerged as a powerful tool for enabling the automatic selection of ML methods and parameter settings for the prediction of biomedical endpoints. Here, we apply the tree-based pipeline optimization tool (TPOT) to predict angiographic diagnoses of coronary artery disease (CAD). With TPOT, ML models are represented as expression trees and optimal pipelines discovered using a stochastic search method called genetic programing. We provide some guidelines for TPOT-based ML pipeline selection and optimization-based on various clinical phenotypes and high-throughput metabolic profiles in the Angiography and Genes Study (ANGES). Results We analyzed nuclear magnetic resonance-derived lipoprotein and metabolite profiles in the ANGES cohort with a goal to identify the role of non-obstructive CAD patients in CAD diagnostics. We performed a comparative analysis of TPOT-generated ML pipelines with selected ML classifiers, optimized with a grid search approach, applied to two phenotypic CAD profiles. As a result, TPOT-generated ML pipelines that outperformed grid search optimized models across multiple performance metrics including balanced accuracy and area under the precision-recall curve. With the selected models, we demonstrated that the phenotypic profile that distinguishes non-obstructive CAD patients from no CAD patients is associated with higher precision, suggesting a discrepancy in the underlying processes between these phenotypes. Availability and implementation TPOT is freely available via http://epistasislab.github.io/tpot/. Contact jhmoore@upenn.edu Supplementary information Supplementary data are available at Bioinformatics online.

The study of automatic machine learning base on radiomics of non-focus area in the first chest CT of different clinical types of COVID-19 pneumonia

“…Different from other radiomics studies, the classification technology of Auto-ML used in this study avoids the limitations of manual selection of machine learning classifiers. Feature selection, feature preprocessing, feature construction, model selection and super parameter optimization 17 , 18 are the advantages of TOPT module. Its main code modules are Sklearn and XGBboost, which are commonly used by Auto-ML researchers.…”

The study of non-focus area in the first chest CT of different clinical types of COVID-19 pneumonia: A study on automatic machine learning of Radiomics

“…Different from other radiomics studies, the classi cation technology of Auto-ML used in this study avoids the limitations of manual selection of machine learning classi ers. Feature selection, feature preprocessing, feature construction, model selection and super parameter optimization [13,14] are the advantages of TOPT module. Its main code modules are Sklearn and XGBboost, which are commonly used by Auto-ML researchers.…”

“…github. io/tpot) is a python Auto-ML tool based on genetic algorithm to optimize Auto-ML pipeline [13][14][15] . In the process of Auto-ML, each group's original data is imported into TPOT, and TPOT randomly divides the original data into training set and test set according to the proportion of 8:2.…”

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