Using machine learning approaches to predict high-cost chronic obstructive pulmonary disease patients in China.

Abstract:

:The accurate identification and prediction of high-cost Chronic obstructive pulmonary disease (COPD) patients is important for addressing the economic burden of COPD. The objectives of this study were to use machine learning approaches to identify and predict potential high-cost patients and explore the key variables of the forecasting model, by comparing differences in the predictive performance of different variable sets. Machine learning approaches were used to estimate the medical costs of COPD patients using the Medical Insurance Data of a large city in western China. The prediction models used were logistic regression, random forest (RF), and extreme gradient boosting (XGBoost). All three models had good predictive performance. The XGBoost model outperformed the others. The areas under the ROC curve for Logistic Regression, RF and XGBoost were 0.787, 0.792 and 0.801. The precision and accuracy metrics indicated that the methods achieved correct and reliable results. The results of this study can be used by healthcare data analysts, policy makers, insurers, and healthcare planners to improve the delivery of health services.

journal_name

Health Informatics J

authors

Luo L,Li J,Lian S,Zeng X,Sun L,Li C,Huang D,Zhang W

doi

10.1177/1460458219881335

subject

Has Abstract

pub_date

2020-09-01 00:00:00

pages

1577-1598

issue

3

eissn

1460-4582

issn

1741-2811

journal_volume

26

pub_type

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