ArticleComputational and mathematical methods in medicine2022
Prediction of Bronchopneumonia Inpatients' Total Hospitalization Expenses Based on BP Neural Network and Support Vector Machine Models.
Article in Computational and mathematical methods in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Machine learning approaches for predicting preventive maintenance costs of expressways in Xinjiang.PloS one · 2026Article
- Development of an upper limb muscle strength rehabilitation assessment system using particle swarm optimisation.Frontiers in bioengineering and biotechnology · 2025Article
- Development of a System for Predicting Hospitalization Time for Patients With Traumatic Brain Injury Based on Machine Learning Algorithms: User-Centered Design Case Study.JMIR human factors · 2024Article
- Prediction and the influencing factor study of colorectal cancer hospitalization costs in China based on machine learning-random forest and support vector regression: a retrospective study.Frontiers in public health · 2024Article
- Design of upper limb muscle strength assessment system based on surface electromyography signals and joint motion.Frontiers in neurology · 2024Article
- Optimization Design of Drilling Fluid Chemical Formula Based on Artificial Intelligence.Computational intelligence and neuroscience · 2022Article
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Abstract
Objective: BP neural network (BPNN) model and support vector machine (SVM) model were used to predict the total hospitalization expenses of patients with bronchopneumonia. Methods: A total of 355 patients with bronchopneumonia from January 2018 to December 2020 were collected and sorted out. The data set was randomly divided into a training set ( Results: The top three influencing factors and their importance for predicting total hospitalization cost by the BPNN model were hospitalization days (0.477), age (0.154), and discharge department (0.083). The top 3 factors predicted by the SVM model were hospitalization days (0.215), age (0.196), and marital status (0.172). The area under the curve of these two models is 0.838 (95% CI: 0.755~0.921) and 0.889 (95% CI: 0.819~0.959), respectively. Conclusion: Both the BPNN model and SVM model can predict the total hospitalization expenses of patients with bronchopneumonia, but the prediction effect of the SVM model is better than the BPNN model.
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