Machine learning algorithms for integrating clinical features to predict intracranial hemorrhage in patients with acute leukemia

Abstract

Background

Intracranial hemorrhage (ICH) in acute leukemia (AL) patients leads to high morbidity and mortality, treatment approaches for ICH are generally ineffective. Thus, early identification of which subjects are at high risk of ICH is of key importance. Currently, machine learning can achieve well predictive capability through constructing algorithms that simultaneously exploit the information coming from clinical features.

Methods

After rigid data preprocessing, 42 different clinical features from 948 AL patients were used to train different machine learning algorithms. We used the feature selection algorithms to select the top 10 features from 42 clinical features. To test the performance of the machine learning algorithms, we calculated area under the curve (AUC) values from receiver operating characteristic (ROC) curves along with 95% confidence intervals (CIs) by cross-validation.

Results

With the 42 features, RF exhibited the best predictive power. After feature selection, the top 10 features were international normalized ratio (INR), prothrombin time (PT), creatinine (Cr), indirect bilirubin (IBIL), albumin (ALB), monocyte (MONO), platelet (PLT), lactic dehydrogenase (LDH), fibrinogen (FIB) and prealbumin (PA). Among the top 10 features, INR, PT, Cr, IBIL and ALB had high predictive performance with an AUC higher than 0.8 respectively.

Conclusions

The RF algorithm exhibited a higher cross-validated performance compared with the classical algorithms, and the selected important risk features should help in individualizing aggressive treatment in AL patients to prevent ICH. Efforts that will be made to test and optimize in independent samples will warrant the application of such algorithm and predictors in the future.

Keywords:

• Acute leukemia
• intracranial hemorrhage
• machine learning
• random forest
• risk
• personalized medicine

Acknowledgments

The authors thank Yongbo Wang, Huiheng Lin, Wenli Liang, Xiong Chen and Chaohua Zhu for their selfless help during machine learning algorithms. Finally, we greatly appreciate the editors and reviewers for their useful comments on the earlier versions of this article.

Authors’ contributions

Chao Qin, Yanyan Tang participated in the study design. Quanhong Chu, Wenxin Wei, Huan Lao, Yujian Li, Yafu Tan, Xiaoyong Wei and Baozi Huang participated in data collection and analysis. Chao Qin, Yanyan Tang, Quanhong Chu and Wenxin Wei participated in editing and proofreading. All authors read and approved the final manuscript.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Consent for publication

We obtained consent for publication of the data in this manuscript from all participating patients.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Ethics approval and consent to participate

Data in this study were collected from the first affiliated hospital of Guangxi medical university, China. The study protocol was conducted in accordance with the standards set by the Helsinki Declaration and local legislation and approved by the Committee of Medical Ethics of the first affiliated hospital of Guangxi medical university.

Additional information

Funding

This research was supported by Youth Program of National Natural Science Foundation of China (Grant No. 82001252) and Natural Science Foundation of Guangxi Province of China (Grant No.2020JJB140052. No.2018JJD140078) and National Natural Science Foundation of China (Grant No. 81860222) and ‘Medical Excellence Award’ funded by the Creative Research Development Grant from the First Affiliated Hospital of Guangxi Medical University.