Explainable Machine Learning for Atrial Fibrillation in the General Population Using a Generalized Additive Model　― A Cross-Sectional Study ―

Figures

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  Figure 1.. Process used to develop the machine learning model. The Kanazawa Medical Association (KMA) database containing 4,386 subjects with atrial fibrillation (AF) and 133,535 subjects with a normal electrocardiogram (ECG) was analyzed. The training dataset comprised subjects from 2009 to 2017, whereas the test dataset comprised subjects for whom data was obtained in 2018. After under-sampling and bootstrap sampling, the dataset was split into a training and out-of-bag (OOB) subset. A generalized additive model (GAM) or generalized linear model (GLM) was constructed using the training data, and the importance score was calculated using the OOB data. In all, 100 small models were developed as an ensemble, and their generalization performance was evaluated by using the test dataset.

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  Figure 2.. The area under the receiver operating characteristic (AUC) curve, with sensitivity and specificity, for the prediction of atrial fibrillation (AF) with the generalized additive model (GAM; blue line) and the generalized linear model (GLM; orange line).

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  Figure 3.. The probability of atrial fibrillation (AF), as determined by the generalized additive model (GAM; red lines) or generalized linear model (GLM; blue lines), according to 9 clinical variables, namely: (A) arrhythmia on physical examination (Exam), (B) past coronary artery disease (CAD), (C) age, (D) hematocrit, (E) γ-glutamyl transpeptidase (γ-GTP), (F) creatinine, (G) hemoglobin, (H) systolic blood pressure (SBP), and (I) HbA1c. Arrhythmia on Exam (A) and past CAD (B) are regarded as binary variables, with trends shown as the mean±SD. The remaining parameters (C–I) are regarded as continuous variables, with trends indicated by solid lines. The histograms show data distribution for each parameter (lighter shading indicating subjects with a normal electrocardiogram [ECG] and darker shading indicating AF subjects). The left axes show the AF risk transformed by the sigmoid function, and the right axes show data distribution. The closer the AF risk is to 1, the higher the likelihood of subclinical AF; conversely, the closer the risk is to 0, the less likely the parameter is associated with AF. Trends within a small distribution of data may be unreliable. In the GAM, suspicion of…