A Machine-Learning Method of Predicting Vital Capacity Plateau Value for Ventilatory Pump Failure Based on Data Mining

• Published in: Healthcare (Basel) 2021-09, Vol.9 (10), p.1306
• Main Authors: Chang, Wenbing, Ji, Xinpeng, Wang, Liping, Liu, Houxiang, Zhang, Yue, Chen, Bang, Zhou, Shenghan
• Format: Article
• Language: English
• Subjects: Accuracy; Atrophy; biomedical engineering; Brain research; Classification; Congenital diseases; disease prediction; Disease prevention; Electromyography; Feature selection; LightGBM; Machine learning; Medical records; Methods; Muscular dystrophy; Nervous system; Neuromuscular diseases; Patients; Pediatrics; RFECV; Support vector machines; ventilatory pump failure; vital capacity plateau value
• ISSN: 2227-9032; 2227-9032
• DOI: 10.3390/healthcare9101306

Ventilatory pump failure is a common cause of death for patients with neuromuscular diseases. The vital capacity plateau value (VCPLAT) is an important indicator to judge the status of ventilatory pump failure for patients with congenital myopathy, Duchenne muscular dystrophy and spinal muscular atrophy. Due to the complex relationship between VCPLAT and the patient's own condition, it is difficult to predict the VCPLAT for pediatric disease from a medical perspective. We established a VCPLAT prediction model based on data mining and machine learning. We first performed the correlation analysis and recursive feature elimination with cross-validation (RFECV) to provide high-quality feature combinations. Based on this, the Light Gradient Boosting Machine (LightGBM) algorithm was to establish a prediction model with powerful performance. Finally, we verified the validity and superiority of the proposed method via comparison with other prediction models in similar works. After 10-fold cross-validation, the proposed prediction method had the best performance and its explained variance score (EVS), mean absolute error (MAE), mean squared error (MSE), root mean square error (RMSE), median absolute error (MedAE) and R were 0.949, 0.028, 0.002, 0.045, 0.015 and 0.948, respectively. It also performed well on test datasets. Therefore, it can accurately and effectively predict the VCPLAT, thereby determining the severity of the condition to provide auxiliary decision-making for doctors in clinical diagnosis and treatment.