Article Details

Abstract

In the field of medical and health care, data-driven decision support systems have become critical tools for enhancing diagnostic accuracy, improving treatment outcomes, and optimizing the allocation of medical resources. However, due to the complexity and diversity of medical data, how to accurately and efficiently extract the value embedded in these data has emerged as a prominent issue. As a widely used machine learning method, the decision tree algorithm is characterized by intuitiveness, ease of comprehension, ease of implementation, and the capability to handle nonlinear relationships. It exhibits strong advantages in addressing classification and regression problems, and has been extensively applied in the medical and health domain. This study focuses on reviewing the applications of decision tree algorithms in disease decision analysis, medical image processing, and medical systems over recent years, aiming to provide a reference framework and methodological guidance for future medical and health researchers to apply decision tree algorithms.

Keywords

decision tree algorithm; disease decision analysis; medical image analysis; medical system

References

1. [1] Wu Shengyong, He Qian, Guo Yibin, etc The current status and prospects of machine learning algorithms in the diagnosis and treatment of stroke [J]. China Health Statistics, 2021, 38 (3): 464-467
2. [2] Zhang Shunxiang Application of Decision Tree Model in Formulating Disease Prevention and Control Strategies (Part 1) [J]. China Health Statistics, 1991 (2): 52-54
3. [3] Su Xiangfei, Lin Guosheng, Li Hongzheng, etc Research on Decision Tree Model of Liver Yang Hyperactivity Syndrome in Primary Hypertension [J]. Chinese Journal of Traditional Chinese Medicine Information, 2024, 31 (10): 157-163
4. [4] Sun Xiangdong, Dong Changzheng, Chen Xiaoyan, etc Application of decision tree in differential diagnosis of primary liver cancer [J]. Journal of Medical Informatics, 2015, 36 (9): 56-59
5. [5] Wu Yafei, Fang Ya Progress in the Application of Machine Learning Methods in Chronic Disease Research [J]. China Health Statistics, 2020, 37 (4): 624-628
6. [6] Ilyas H，Sajid A，Mahvish P，et al. Chronic kidney disease diagnosis using decision tree algorithms [J/OL].BMC Nephrology，2021，22（1）：273.https://link.springer.com/article/10.1186/s12882-021-02474-z.
7. [7] Yin Qingyan, Che Lumei, Liu Xingyu Application of decision tree ensemble learning in cancer risk stratification [J]. Computer and Digital Engineering, 2024, 52 (1): 169-173189
8. [8] Jiang Xingli, Wang Jianhui Disease classification prediction based on decision tree algorithm [J]. Information and Computer (Theoretical Edition), 2021, 33 (11): 51-53
9. [9] Xu Fei, Zhu Guangxi, Wang Kexin Constructing a prediction model for the risk of radiation-induced oral mucositis in nasopharyngeal carcinoma patients after radiotherapy based on decision tree algorithm [J]. Journal of Otolaryngology, Shandong University, 2025, 39 (4): 42-48
10. [10] Maimaitituerxun R，Chen W H，Xiang J S，et al. Predictive model for identifying mild cognitive impairment in patients with type 2 diabetes mellitus：A CHAID decision tree analysis [J/OL].Brain and behavior，2024，14（3）：e3456.https://onlinelibrary.wiley.com/doi/10.1002/brb3.3456.
11. [11] Hou Yumei, Zhu Yanan, Zhu Lichun, etc Application of decision tree model in risk prediction of type 2 diabetes [J]. China Health Statistics, 2016, 33 (6): 976-978982
12. [12] Ren Shoupeng, Li Jin, Wang Jingru, etc LncRNA disease association prediction method based on ensemble regression decision tree [J]. Computer Science, 2022, 49 (2): 265-271
13. [13] Böttcher L，Breedvelt J J F，Warren F C，et al. Identifying Relapse Predictors in Individual Participant Data With Decision Trees [J].BMC Psychiatry，2023，23（1）：1-12.
14. [14] Liu Bing, Li Ping, Zhu Meiye, etc Application of decision tree model and logistic regression model in the analysis of influencing factors of intervention effect in high-risk population of gastric cancer [J]. China Health Statistics, 2018, 35 (1): 70-73
15. [15] Hu Dengmin, Xu Run, Li Jian, etc Application of decision tree and logistic regression models to analyze the influencing factors of surgical site infection after gastrointestinal perforation repair [J]. Chinese Journal of Infection Control, 2024, 23 (7): 826-832
16. [16] Wang Y，Dou L，Wang N，et al. An Analysis of Factors Influencing Cognitive Dysfunction Among Older Adults in Northwest China Based on Logistic Regression and Decision Tree Modelling [J].BMC Geriatrics，2024，24（1）：1-12.
17. [17] Gheondea-Eladi A. Patient Decision Aids: A Content Analysis Based on a Decision Tree Structure [J].BMC Medical Informatics and Decision Making，2019，19（1）：1-10.
18. [18] Johnson A，Cooper G F，Visweswaran S. Patient-Specific Modeling With Personalized Decision Paths [J].AMIA Annual Symposium Proceedings，2020，2020：602-611.
19. [19] Bai Y，Yao H，Jiang X，et al. Construction of a Non-Mutually Exclusive Decision Tree for Medication Recommendation of Chronic Heart Failure [J/OL].Frontiers in Pharmacology，2022，12：1-11.https://doi.org/10.3390/ph17060795.
20. [20] Serrano-Aguilar P，Abreu R，Antón-Canalís L，et al. Development and Validation of a Computer-Aided Diagnostic Tool to Screen for Age-Related Macular Degeneration by Optical Coherence Tomography [J].British Journal of Ophthalmology，2012，96（4）：503-507.
21. [21] Naralan M E，Cakir B，Orhan K. The Utilization of Decision Trees on Orthopantomographic and Lateral Panoramic Graphs for the Diagnosis of Unilateral Anterior Disc Displacement of the Temporomandibular Joint [J].BMC Oral Health，2024，24（1）：1-10.
22. [22] Jia D，Li X，Zhang Q，et al. A Decision Tree Built With Parameters Obtained by Computed Tomographic Pulmonary Angiography Is Useful for Predicting Adverse Outcomes in Non-High-Risk Acute Pulmonary Embolism Patients [J].Respiratory Research，2019，20（1）：1-10.
23. [23] Mudali D，Teune L K，Renken R J，et al. Classification of Parkinsonian Syndromes From FDG-PET Brain Data Using Decision Trees With SSM/PCA Features [J].Computational and Mathematical Methods in Medicine，2015，2015：1-10.
24. [24] Rismayanti N，Azzahrah S F. Classification of Skin Diseases Using Decision Tree Algorithm on an Imbalanced Dataset [J].International Journal of Artificial Intelligence in Medical Issues，2024，2（2）：83-94.
25. [25] Ren Yi Research on the Mining Method of Massive Medical Image Data Based on Decision Trees [J]. Electronic Design Engineering, 2019, 27 (6): 33-36, 41
26. [26] Wu Tong, Zhang Tao, Zhang Han, etc Analysis of hospitalization expenses for Parkinson's patients based on DRGs payment method [J]. China Health Statistics, 2024, 41 (2): 257-260
27. [27] Liu Chun, Wang Huaibin, Wu Jia Research on the inpatient cost standard of type 2 diabetes patients in tertiary general hospitals based on DRGs [J]. China Health Statistics, 2024,41 (1): 139-142
28. [28] Wu S，Pan Q，Chen T. Research on Diagnosis-Related Group Grouping of Inpatient Medical Expenditure in Colorectal Cancer Patients Based on a Decision Tree Model [J].World Journal of Clinical Cases，2020，8（12）：2484-2493.
29. [29] Chern C，Chen Y，Hsiao B. Decision Tree–Based Classifier in Providing Telehealth Service [J].BMC Medical Informatics and Decision Making，2019，19（1）：1-10.
30. [30] Keikes L，Kos M，Verbeek X A A M，et al. Conversion of a Colorectal Cancer Guideline Into Clinical Decision Trees With Assessment of Validity [J].International Journal for Quality in Health Care，2021，33（2）：1-8.
31. Gong Lejun, Zhang Lipeng, Li Yuqian, etc Mining and decision-making of breast cancer case history text based on decision tree [J]. Journal of Nanjing Normal University (Natural Science Edition), 2019, 42 (3): 42-51
32. Jiang Mingyin, Liu Shenglin, Cheng Ju, etc Application Research on Development Analysis of Medical Device Industry Based on Decision Tree [J]. Medical and Health Equipment, 2016, 37 (3): 23-26
33. [33] Chen J，Wu L，Liu K，et al. EDST: A Decision Stump Based Ensemble Algorithm for Synergistic Drug Combination Prediction [J].BMC Bioinformatics，2023，24（1）：1-15.
34. [34] Durrant J D，Amaro R E. Machine-Learning Techniques Applied to Antibacterial Drug Discovery [J].Chemical Biology & Drug Design，2015，85（1）：14-21.
35. [35] Toni E，Ayatollahi H，Abbaszadeh R，et al. Machine Learning Techniques for Predicting Drug-Related Side Effects: A Scoping Review [J].Pharmaceuticals，2024，17（6）：795.
36. [36] Hammann F，Gutmann H，Vogt N，et al. Prediction of Adverse Drug Reactions Using Decision Tree Modeling [J].Clinical Pharmacology & Therapeutics，2010，88（1）：52-59.

Funding

National Natural Science Foundation of China (82360992); Key Research Laboratory of Cancer Formula and Syndrome Information Mining, Jiangxi Provincial Administration of Traditional Chinese Medicine (KP20221002); Key Project of Science and Technology Plan of Jiangxi Provincial Administration of Traditional Chinese Medicine (2022Z007)

About the authors

Zhang Xinyou (1962-), male, Han ethnicity, from De'an, Jiangxi, professor, doctoral supervisor, doctoral student, research direction: pharmaceutical data mining; Zou Ziwen (2001-), male, Han ethnicity, from Yichun, Jiangxi Province, is currently pursuing a master's degree in pharmaceutical data mining; Zhou Zhitao (2001-), male, Han ethnicity, from Yingtan, Jiangxi Province, is currently pursuing a master's degree in Traditional Chinese Medicine Data Mining; Zhou Xiaoling (1992-), female, Han ethnicity, from Zhongxiang, Hubei, lecturer, doctoral student, research direction: pharmaceutical data mining; Corresponding author: Zhang Chunqiang (1981-), male, Han ethnicity, from Tangshan, Hebei Province, associate professor, master's student, research direction: pharmaceutical data mining.