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 Jjournal_title
Health informatics journalauthors
Luo L,Li J,Lian S,Zeng X,Sun L,Li C,Huang D,Zhang Wdoi
10.1177/1460458219881335subject
Has Abstractpub_date
2020-09-01 00:00:00pages
1577-1598issue
3eissn
1460-4582issn
1741-2811journal_volume
26pub_type
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