Machine Learning and Regression Analysis to Model the Length of Hospital Stay in Patients with Femur Fracture
Fractures of the femur are a frequent problem in elderly people, and it has been demonstrated that treating them with a diagnostic–therapeutic–assistance path within 48 h of admission to the hospital reduces complications and shortens the length of the hospital stay (LOS). In this paper, the preoper...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/9/4/172 |
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| author | Carlo Ricciardi Alfonso Maria Ponsiglione Arianna Scala Anna Borrelli Mario Misasi Gaetano Romano Giuseppe Russo Maria Triassi Giovanni Improta |
| author_facet | Carlo Ricciardi Alfonso Maria Ponsiglione Arianna Scala Anna Borrelli Mario Misasi Gaetano Romano Giuseppe Russo Maria Triassi Giovanni Improta |
| author_sort | Carlo Ricciardi |
| collection | DOAJ |
| description | Fractures of the femur are a frequent problem in elderly people, and it has been demonstrated that treating them with a diagnostic–therapeutic–assistance path within 48 h of admission to the hospital reduces complications and shortens the length of the hospital stay (LOS). In this paper, the preoperative data of 1082 patients were used to further extend the previous research and to generate several models that are capable of predicting the overall LOS: First, the LOS, measured in days, was predicted through a regression analysis; then, it was grouped by weeks and was predicted with a classification analysis. The KNIME analytics platform was applied to divide the dataset for a hold-out cross-validation, perform a multiple linear regression and implement machine learning algorithms. The best coefficient of determination (R<sup>2</sup>) was achieved by the support vector machine (R<sup>2</sup> = 0.617), while the mean absolute error was similar for all the algorithms, ranging between 2.00 and 2.11 days. With regard to the classification analysis, all the algorithms surpassed 80% accuracy, and the most accurate algorithm was the radial basis function network, at 83.5%. The use of these techniques could be a valuable support tool for doctors to better manage orthopaedic departments and all their resources, which would reduce both waste and costs in the context of healthcare. |
| first_indexed | 2024-03-09T11:09:07Z |
| format | Article |
| id | doaj.art-7877a28b1bfe490386aede40b2fbdd28 |
| institution | Directory Open Access Journal |
| issn | 2306-5354 |
| language | English |
| last_indexed | 2024-03-09T11:09:07Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj.art-7877a28b1bfe490386aede40b2fbdd282023-12-01T00:49:39ZengMDPI AGBioengineering2306-53542022-04-019417210.3390/bioengineering9040172Machine Learning and Regression Analysis to Model the Length of Hospital Stay in Patients with Femur FractureCarlo Ricciardi0Alfonso Maria Ponsiglione1Arianna Scala2Anna Borrelli3Mario Misasi4Gaetano Romano5Giuseppe Russo6Maria Triassi7Giovanni Improta8Department of Electrical Engineering and Information Technology, University of Naples “Federico II”, 80125 Naples, ItalyDepartment of Electrical Engineering and Information Technology, University of Naples “Federico II”, 80125 Naples, ItalyDepartment of Public Health, University Hospital of Naples “Federico II”, 80131 Naples, ItalyHealth Department, University Hospital of Salerno “San Giovanni di Dio e Ruggi d′Aragona”, 84126 Salerno, ItalyDepartment of the Orthopaedics, National Hospital (A.O.R.N.) Antonio Cardarelli, 80131 Naples, ItalyDepartment of the Orthopaedics, National Hospital (A.O.R.N.) Antonio Cardarelli, 80131 Naples, ItalyNational Hospital (A.O.R.N.) Antonio Cardarelli, 80131 Naples, ItalyDepartment of Public Health, University Hospital of Naples “Federico II”, 80131 Naples, ItalyDepartment of Public Health, University Hospital of Naples “Federico II”, 80131 Naples, ItalyFractures of the femur are a frequent problem in elderly people, and it has been demonstrated that treating them with a diagnostic–therapeutic–assistance path within 48 h of admission to the hospital reduces complications and shortens the length of the hospital stay (LOS). In this paper, the preoperative data of 1082 patients were used to further extend the previous research and to generate several models that are capable of predicting the overall LOS: First, the LOS, measured in days, was predicted through a regression analysis; then, it was grouped by weeks and was predicted with a classification analysis. The KNIME analytics platform was applied to divide the dataset for a hold-out cross-validation, perform a multiple linear regression and implement machine learning algorithms. The best coefficient of determination (R<sup>2</sup>) was achieved by the support vector machine (R<sup>2</sup> = 0.617), while the mean absolute error was similar for all the algorithms, ranging between 2.00 and 2.11 days. With regard to the classification analysis, all the algorithms surpassed 80% accuracy, and the most accurate algorithm was the radial basis function network, at 83.5%. The use of these techniques could be a valuable support tool for doctors to better manage orthopaedic departments and all their resources, which would reduce both waste and costs in the context of healthcare.https://www.mdpi.com/2306-5354/9/4/172machine learningmultiple linear regressionclinical pathwayorthopaedic |
| spellingShingle | Carlo Ricciardi Alfonso Maria Ponsiglione Arianna Scala Anna Borrelli Mario Misasi Gaetano Romano Giuseppe Russo Maria Triassi Giovanni Improta Machine Learning and Regression Analysis to Model the Length of Hospital Stay in Patients with Femur Fracture Bioengineering machine learning multiple linear regression clinical pathway orthopaedic |
| title | Machine Learning and Regression Analysis to Model the Length of Hospital Stay in Patients with Femur Fracture |
| title_full | Machine Learning and Regression Analysis to Model the Length of Hospital Stay in Patients with Femur Fracture |
| title_fullStr | Machine Learning and Regression Analysis to Model the Length of Hospital Stay in Patients with Femur Fracture |
| title_full_unstemmed | Machine Learning and Regression Analysis to Model the Length of Hospital Stay in Patients with Femur Fracture |
| title_short | Machine Learning and Regression Analysis to Model the Length of Hospital Stay in Patients with Femur Fracture |
| title_sort | machine learning and regression analysis to model the length of hospital stay in patients with femur fracture |
| topic | machine learning multiple linear regression clinical pathway orthopaedic |
| url | https://www.mdpi.com/2306-5354/9/4/172 |
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