Predicting atrial fibrillation in comorbid patients with arterial hypertension and chronic obstructive pulmonary disease using laboratory research methods: a machine learning approach

Abstract

BACKGROUND: Arterial hypertension and chronic obstructive pulmonary disease have a deleterious effect on the structure of the heart, leading to the development of atrial fibrillation, which remains the leading cause of cerebral stroke and premature death [1]. Consequently, the early identification of atrial fibrillation risk factors in patients with arterial hypertension and chronic obstructive pulmonary disease is of paramount importance for the prevention of such conditions. This is why predictive cardiology employs machine learning methods, which are demonstrably superior to classical statistical methods of prediction [2–4].

AIM: The study aimed to develop a prognostic model of atrial fibrillation in comorbid patients with arterial hypertension and chronic obstructive pulmonary disease based on multilayer perceptron.

MATERIALS AND METHODS: The study included 419 patients treated at the University Clinical Hospital No. 4 of the I.M. Sechenov First Moscow State Medical University. Group 1 consisted of 91 (21.7%) patients with a verified diagnosis of atrial fibrillation, while Group 2 comprised 328 (78.3%) patients without atrial fibrillation. The random forest machine learning algorithm was used to identify predictors, which were then utilized to develop a neural network of the multilayer perceptron type. This consisted of two layers: an input layer of 12 neurons with the ReLU activation function and an output layer that receives input data from the previous layer and transmits them to one output with the sigmoid activation function. The threshold value, sensitivity, specificity, and diagnostic efficiency of the obtained model were determined using receiver operating characteristic analysis with the calculation of the area under the curve (AUC).

RESULTS: By the first stage of prognostic model development, the most significant predictors of atrial fibrillation development were selected by the random forest machine learning algorithm. The model was developed using three variables: C-reactive protein concentration (odds ratio, OR 1.04; 95% confidence interval, CI 1.015–1.067; p=0.002), erythrocyte sedimentation rate (OR 1.04; 95% CI 1.019–1.069; p=0.002), and creatinine concentration (OR 1.03; 95% CI 1.011–1.042; p <0.001). These variables were used to train a multilayer perceptron model on a test sample for 500 epochs.

Following training, the developed model exhibited a sensitivity of 85%, a specificity of 80%, and a diagnostic efficiency of 79.6%. AUC amounted to 0.900.

CONCLUSIONS: The study resulted in the development of a prognostic model based on the application of machine learning methods, which exhibited favorable metrics. This model may be considered a valuable tool for clinical practice.

Keywords

atrial fibrillation / prognosis / machine learning / neural network

References

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