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. 2022 Feb 22;12(3):328.
doi: 10.3390/jpm12030328.

A Machine Learning Approach to Predict the Rehabilitation Outcome in Convalescent COVID-19 Patients

Sarah Adamo  1   2 , Pasquale Ambrosino  3 , Carlo Ricciardi  1   2 , Mariasofia Accardo  4 , Marco Mosella  4 , Mario Cesarelli  1   2 , Giovanni d'Addio  1 , Mauro Maniscalco  4
Affiliations

A Machine Learning Approach to Predict the Rehabilitation Outcome in Convalescent COVID-19 Patients

Sarah Adamo et al. J Pers Med. .

Abstract

Background: After the acute disease, convalescent coronavirus disease 2019 (COVID-19) patients may experience several persistent manifestations that require multidisciplinary pulmonary rehabilitation (PR). By using a machine learning (ML) approach, we aimed to evaluate the clinical characteristics predicting the effectiveness of PR, expressed by an improved performance at the 6-min walking test (6MWT).

Methods: Convalescent COVID-19 patients referring to a Pulmonary Rehabilitation Unit were consecutively screened. The 6MWT performance was partitioned into three classes, corresponding to different degrees of improvement (low, medium, and high) following PR. A multiclass supervised classification learning was performed with random forest (RF), adaptive boosting (ADA-B), and gradient boosting (GB), as well as tree-based and k-nearest neighbors (KNN) as instance-based algorithms.

Results: To train and validate our model, we included 189 convalescent COVID-19 patients (74.1% males, mean age 59.7 years). RF obtained the best results in terms of accuracy (83.7%), sensitivity (84.0%), and area under the ROC curve (94.5%), while ADA-B reached the highest specificity (92.7%).

Conclusions: Our model enables a good performance in predicting the rehabilitation outcome in convalescent COVID-19 patients.

Keywords: COVID-19; chronic disease; disability; exercise; machine learning; occupational medicine; outcome; rehabilitation.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Receiver Operating Characteristic (ROC) curve of RF algorithm (blue line); ROC = 0.5, threshold for considering the model better than random guessing (black line).
Figure 2
Figure 2
Confusion matrix of random forest (RF) algorithm.
See this image and copyright information in PMC

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