Model for Predicting the Risk of Bronchopleural Fistula After Pneumonectomy for Destructive Pulmonary Tuberculosis
Introduction: Predicting various events based on influencing factors is important for statistical analysis in medical research. Unfortunately, mathematical models are rarely built on the identified factors. Objective: To develop a model to predict the risk of bronchopleural fistula after pneumonecto...
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Veröffentlicht in: | Innovacionnaâ medicina Kubani (Online) 2023-11 (4), p.60-67 |
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Format: | Artikel |
Sprache: | eng ; rus |
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Zusammenfassung: | Introduction:
Predicting various events based on influencing factors is important for statistical analysis in medical research. Unfortunately, mathematical models are rarely built on the identified factors.
Objective:
To develop a model to predict the risk of bronchopleural fistula after pneumonectomy for destructive pulmonary tuberculosis.
Materials and methods:
We analyzed medical records of 198 patients who underwent pneumonectomy. Of them 6 patients (3%) developed a bronchopleural fistula. We used machine learning algorithms such as ridge regression, support vector machine, random forest, and CatBoost, the Jupyter opensource development environment, and Python 3.6 to build prediction models. ROC analysis was used to evaluate the quality of the binary classification.
Results:
We built 4 models to predict the risk of bronchopleural fistula. Their ROC AUC were as follows: ridge regression – 0.88, support vector machine – 0.87, CatBoost – 0.75, and random forest – 0.74. The model based on the ridge regression showed the best ROC AUC. Based on the coordinates of the ROC curve, the threshold value of 1.9% provides the maximum total sensitivity and specificity (100% and 68.8%, respectively).
Conclusions:
The developed model has a high predictive ability, which allows focusing on the patient group with an increased risk of bronchopleural fistula and justifying the need for preventive measures. |
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ISSN: | 2541-9897 2541-9897 |
DOI: | 10.35401/2541-9897-2023-8-4-60-67 |