Increasing transparency in machine learning through bootstrap simulation and shapely additive explanations

• Published in: PloS one Vol. 18; no. 2; p. e0281922
• Main Authors: Huang, Alexander A., Huang, Samuel Y.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 23.02.2023; Public Library of Science (PLoS)
• Subjects: Accuracy; Age; Algorithms; Angina; Angina pectoris; Artificial neural networks; Biology and Life Sciences; Blood pressure; Bootstrapping (Statistics); Cardiovascular disease; Cardiovascular diseases; Choice learning; Cholesterol; Computer and Information Sciences; Computer Simulation; Datasets; Diagnosis; Electrocardiography; Evaluation; Fasting; Health services; Heart diseases; Heart rate; Humans; Learning algorithms; Machine Learning; Males; Medicine and Health Sciences; Methods; Model accuracy; Modelling; Neural networks; Neural Networks, Computer; Physical Sciences; Regression analysis; Reliability analysis; Reproducibility of Results; Research and Analysis Methods; Simulation; Simulation methods; Statistical methods; Statistics; Transparency; Variables; Variance; United States; United Kingdom > UK; England
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0281922

Machine learning methods are widely used within the medical field. However, the reliability and efficacy of these models is difficult to assess, making it difficult for researchers to identify which machine-learning model to apply to their dataset. We assessed whether variance calculations of model metrics (e.g., AUROC, Sensitivity, Specificity) through bootstrap simulation and SHapely Additive exPlanations (SHAP) could increase model transparency and improve model selection. Data from the England National Health Services Heart Disease Prediction Cohort was used. After comparison of model metrics for XGBoost, Random Forest, Artificial Neural Network, and Adaptive Boosting, XGBoost was used as the machine-learning model of choice in this study. Boost-strap simulation (N = 10,000) was used to empirically derive the distribution of model metrics and covariate Gain statistics. SHapely Additive exPlanations (SHAP) to provide explanations to machine-learning output and simulation to evaluate the variance of model accuracy metrics. For the XGBoost modeling method, we observed (through 10,000 completed simulations) that the AUROC ranged from 0.771 to 0.947, a difference of 0.176, the balanced accuracy ranged from 0.688 to 0.894, a 0.205 difference, the sensitivity ranged from 0.632 to 0.939, a 0.307 difference, and the specificity ranged from 0.595 to 0.944, a 0.394 difference. Among 10,000 simulations completed, we observed that the gain for Angina ranged from 0.225 to 0.456, a difference of 0.231, for Cholesterol ranged from 0.148 to 0.326, a difference of 0.178, for maximum heart rate (MaxHR) ranged from 0.081 to 0.200, a range of 0.119, and for Age ranged from 0.059 to 0.157, difference of 0.098. Use of simulations to empirically evaluate the variability of model metrics and explanatory algorithms to observe if covariates match the literature are necessary for increased transparency, reliability, and utility of machine learning methods. These variance statistics, combined with model accuracy statistics can help researchers identify the best model for a given dataset.