Article
A Machine-Learning Method of Predicting Vital Capacity
Plateau Value for Ventilatory Pump Failure Based on
Data Mining
Wenbing Chang
1
, Xinpeng Ji
1
, Liping Wang
2
, Houxiang Liu
1
, Yue Zhang
1
, Bang Chen
1
and Shenghan Zhou
1,
*
Citation: Chang, W.; Ji, X.; Wang, L.;
Liu, H.; Zhang, Y.; Chen, B.; Zhou, S.
A Machine-Learning Method of
Predicting Vital Capacity Plateau
Value for Ventilatory Pump Failure
Based on Data Mining. Healthcare
2021, 9, 1306. https://doi.org/
10.3390/healthcare9101306
Academic Editors: Keun Ho Ryu and
Nipon Theera-Umpon
Received: 6 August 2021
Accepted: 26 September 2021
Published: 30 September 2021
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4.0/).
1
School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China;
changwenbing@buaa.edu.cn (W.C.); sy1914103@buaa.edu.cn (X.J.); zy1914125@buaa.edu.cn (H.L.);
Zhangyue1127@buaa.edu.cn (Y.Z.); bang@buaa.edu.cn (B.C.)
2
Department of Neurology, Peking University Third Hospital, Beijing 100191, China; abn8360@gmail.com
* Correspondence: zhoush@buaa.edu.cn; Tel.: +86-10-8231-7804
Abstract:
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
2
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.
Keywords:
ventilatory pump failure; vital capacity plateau value; biomedical engineering; RFECV;
disease prediction; LightGBM
1. Introduction
The central nervous system, peripheral nervous system, neuromuscular tissue and
thorax that drive or regulate respiratory movement are collectively referred to as the
ventilatory pump. Respiratory failure caused by dysfunction in these parts is called venti-
latory pump failure. Pump failure mainly causes patients to have ventilatory dysfunction,
manifested as type II respiratory failure. Common neurological diseases causing ventila-
tory pump failure include brain trauma, brain stroke, brain tumor, encephalitis, myelitis,
motor neuron disease, acute inflammatory polyradiculoneuropathy, myasthenia gravis,
polymyositis, muscular dystrophy and drug poisoning.
Ventilatory pump failure can be an acute on chronic condition that without ventilatory
assistance leads to poor prognosis and even death in patients with critical neurological
diseases. The rapid diagnosis and accurate treatment of ventilatory pump failure can
effectively reduce the mortality of patients and provide opportunities for the recovery of
nervous system function. Therefore, it is important to determine the disease severity for
pediatric diseases to prevent sudden deterioration of the condition. In order to do this, it is
Healthcare 2021, 9, 1306. https://doi.org/10.3390/healthcare9101306 https://www.mdpi.com/journal/healthcare
