Predicting forced vital capacity (FVC) using support vector regression (SVR)

Abstract

Objective: Spirometry, as the gold standard approach in the diagnosis of chronic obstructive pulmonary disease (COPD), has strict end of test (EOT) criteria (e.g. complete exhalation), which cannot be met by patients with compromised health states. Thus, significant parameters measured by spirometry, such as forced vital capacity (FVC), have limited accuracies. To address this issue, the present study aimed to develop models based on support vector regression (SVR) to predict values of FVC under the condition that the EOT criteria were not fully met. Approach: The prediction models for the quantification of FVC were developed based on SVR. A total of 354 subjects underwent conventional spirometry (CS), and the resulting data of forced expiratory volumes in 1 s (FEV1), peak expiratory flow (PEF), age and gender were used as input features, while the resulting values of the FVC were used as the target feature in the prediction models. Next, three prediction models (mixed model, normal model and abnormal model) were established according to the criterion in the diagnosis of COPD that a postbronchodilator shows an FEV1/FVC ratio lower than 0.70. Then, 35 subjects were recruited to be tested using both CS and a low-degree-of-EOT criteria spirometry (LDCS), which did not fully meet the EOT criteria of CS. In LDCS, subjects were allowed to terminate the procedure at their own will at any time after the technicians had assumed that both acceptable values of FEV1 and PEF had been obtained. Quantified values of FVC derived from both CS and LDCS were compared to validate the performances of the developed prediction models. Main results: The FVC prediction performances of the normal model and abnormal model were better than that of the mixed model. The root mean squared error are lower than 0.35 l and the accuracies are higher up to 95%. One-tailed t test results demonstrate that the absolute differences in the measured and predicted values are not significantly different from 0.15 l for both the abnormal model and the normal model. Significance: Our study shows the possibility of predicting FVC with acceptable precision in cases where the EOT criteria of spirometry were not fully met, which can be beneficial for patients who cannot or did not achieve full exhalation in spirometry.

Prediction models for pulmonary function during acute exacerbation of chronic obstructive pulmonary disease

“…When patients with AECOPD cannot complete the pulmonary function test, we can preliminarily judge whether a patient has a severe episode and the predicted values of FEV 1 and FVC can be easily accessed. Compared with the study by Wang et al (2019) in which acceptable FVC values can be obtained by reducing the degrees of the EOT criteria to an extent, this approach achieved comparable accuracy on the basis that patients no longer need to exhale at all, which can be used to predict pulmonary function indexes during AECOPD. Some demographic parameters, including height, may be affected by other factors including osteoporosis; and this will confound the interpretation of comparisons made to a predicted normal value for pulmonary function that has been determined based upon a patient's height.…”

“…The results showed that the model can be used to predict FEV 1 values of normal and abnormal subjects. Wang et al (2019) proposed prediction models based on age, gender, FEV 1 and peak expiratory flow (PEF) to estimate forced vital capacity (FVC) when the end-of-test (EOT) criteria in conventional spirometry was not met. The results of the model operation showed that the measured FVC value was positively correlated with the predicted one, and the Pearson correlation coefficient of the best model was 0.98.…”

Study of the Influence of Data Volume on the Quality of Regression to Restore the Distribution of Temperatures inside Tissue during Hyperthermia

“…The best result was shown by the linear regression model; the coefficient of determination R was equal to 0.991, the average absolute error MAE was equal to 0.014, and the root of the root mean square error RMSE was equal to 0.017. The authors of the scientific work [9] developed a model based on the support vector regression (SVR) method to predict the values of forced vital capacity.…”