Predicting hospitalization costs for pulmonary tuberculosis patients based on machine learning.

ABSTRACT:

Background

Pulmonary tuberculosis (PTB) is a prevalent chronic disease associated with a significant economic burden on patients. Using machine learning to predict hospitalization costs can allocate medical resources effectively and optimize the cost structure rationally, so as to control the hospitalization costs of patients better.

Methods

This research analyzed data (2020-2022) from a Kashgar pulmonary hospital's information system, involving 9570 eligible PTB patients. SPSS 26.0 was used for multiple regression analysis, while Python 3.7 was used for random forest regression (RFR) and MLP. The training set included data from 2020 and 2021, while the test set included data from 2022. The models predicted seven various costs related to PTB patients, including diagnostic co

SUBMITTER: Fan S 

PROVIDER: S-EPMC11360310 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature