Machine Learning and Regression Analysis to Model the Length of Hospital Stay in Patients with Femur Fracture

Abstract

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 (R2) was achieved by the support vector machine (R2 = 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.

[Retracted] Research on Injury Causes and Prevention Effect of College Rowing Athletes Based on Multiple Regression and Residual Algorithm

“…Therefore, the application of multiple regression analysis in sports injuries is more reasonable. Multiple regression analysis is a comprehensive analysis method, which analyzes the structure of rowing competition by using characteristic injury causes, integrates the injury causes, and realizes the prevention and control system [ 4 ]. Multivariate regression is widely used in electric power, computer, and other fields, which can accurately calculate the relationship between rowing races, judge the logic between diagnostic structures, and finally improve the accuracy of calculation results.…”

“…Therefore, the application of multiple regression analysis in sports injuries is more reasonable. Multiple regression analysis is a comprehensive analysis method, which analyzes the structure of rowing competition by using characteristic injury causes, integrates the injury causes, and realizes the prevention and control system [4].…”

Automated Lung-Related Pneumonia and COVID-19 Detection Based on Novel Feature Extraction Framework and Vision Transformer Approaches Using Chest X-ray Images

“…Digital technology has recently grown in importance across the world. Machine learning [ 15 , 16 , 17 , 18 ] and deep learning [ 19 ] can be quite useful in this endeavor. Previously, Heyat et al used medical machine learning to detect sleep disorders, mental stress, and female disorders based on signal and experimental data [ 20 , 21 , 22 , 23 , 24 , 25 ].…”

Deep-Learning-Based Character Recognition from Handwriting Motion Data Captured Using IMU and Force Sensors

“…The authors of [ 11 ] used dynamic time warping to distinguish characters based on their temporal data similarity. However, due to their wide range of application, from finance to construction, from information technology to healthcare application, machine learning methods are becoming increasingly prominent in handwritten character recognition [ 21 , 22 , 23 , 24 , 25 ]. In [ 12 ], a GAN-based machine learning method was used to solve a classification problem, while [ 8 ] describes the use of a deep-learning-trained neural network method to collect sufficient data.…”