A Systematic Review of Decision Trees Application in the Medical Field
Research Article
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A Systematic Review of Decision Trees Application in the Medical Field

Jiaming Xing 1*
1 Beijing Etown Academy, Beijing, 100176, China
*Corresponding author: xjmBJEAINTER@outlook.com
Published on 28 October 2025
Journal Cover
ACE Vol.202
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-80590-497-7
ISBN (Online): 978-1-80590-498-4
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Abstract

In the contemporary field of diagnostic and clinical medical practice, the reliance on big data is steadily deepening. However, it is confronted with challenges such as the exponential growth of data volume and the diversification of data types, which impose new requirements for the efficiency of data processing. Against this backdrop, decision trees play an indispensable and pivotal role in precision medicine and disease diagnosis. Therefore, this study aims to systematically sort out the core application directions and underlying principles of decision trees in the medical field through literature review and case analysis methods, while identifying their existing limitations and future development trends. The research findings indicate that decision trees have been widely applied in fields such as stomatology, cardiovascular diseases, and chronic diseases, achieving remarkably positive outcomes. Their value extends beyond disease diagnosis; they also exert a beneficial and positive impact on optimizing treatment regimens and rationalizing the structure of medical treatment costs. The study further highlights the current problems and future development directions.

Keywords:

Decision Tree, Disease diagnosis, ID3 Algorithm

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Xing,J. (2025). A Systematic Review of Decision Trees Application in the Medical Field. Applied and Computational Engineering,202,47-55.

References

[1]. Mallappallil, M., Sabu, J., Gruessner, A., & Salifu, M. (2020). A review of big data and medical research. SAGE open medicine, 8, 2050312120934839.

[2]. Abdulqader, H. A., & Abdulazeez, A. M. (2024). A review on decision tree algorithm in healthcare applications. The Indonesian Journal of Computer Science, 13(3).

[3]. Slocum, M. (2012). Decision making using id3 algorithm. Insight: River Academic J, 8(2), 1-12.

[4]. Di, J., & Xu, Y. (2019). Decision tree improvement algorithm and its application. International Core Journal of Engineering, 5(9), 151-158.

[5]. Tanner, L., Schreiber, M., Low, J. G., Ong, A., Tolfvenstam, T., Lai, Y. L., ... & Ooi, E. E. (2008). Decision tree algorithms predict the diagnosis and outcome of dengue fever in the early phase of illness. PLoS neglected tropical diseases, 2(3), e196.

[6]. abdulaziz Mohsen, A., Alsurori, M., Aldobai, B., & Mohsen, G. A. (2019). New approach to medical diagnosis using artificial neural network and decision tree algorithm: application to dental diseases. International Journal of Information Engineering and Electronic Business, 11(4), 52-60.

[7]. Shouman, M. (2011). Using decision tree for diagnosing heart disease patients.

[8]. Ilyas, H., Ali, S., Ponum, M., Hasan, O., Mahmood, M. T., Iftikhar, M., & Malik, M. H. (2021). Chronic kidney disease diagnosis using decision tree algorithms. BMC nephrology, 22(1), 273.

[9]. Roy, S., Aithal, P. S., & Bose, D. R. (2021). Judging Mental Health Disorders Using Decision Tree Models. International Journal of Health Sciences and Pharmacy (IJHSP), 5(1), 11-22.

[10]. Liu, W. B., Ren, D. Y., Tao, F., & Chen, G. L. (2018). Optimization of medical treatment process and rule mining for hospitalized burn patients based on decision tree. Academic Journal of Second Military Medical University, 39(12), 5-33

[11]. Wu, S. W., Pan, Q., & Chen, T. (2020). Research on diagnosis-related group grouping of inpatient medical expenditure in colorectal cancer patients based on a decision tree model. World Journal of Clinical Cases, 8(12), 10.

[12]. Chanmee, S., & Kesorn, K. (2020). Data quality enhancement for decision tree algorithm using knowledge-based model. Current Applied Science and Technology, 259-277.

[13]. Gulati, P., Sharma, A., & Gupta, M. (2016). Theoretical study of decision tree algorithms to identify pivotal factors for performance improvement: A review. Int. J. Comput. Appl, 141(14), 19-25.

[14]. Abdulqader, H. A., & Abdulazeez, A. M. (2024). A review on decision tree algorithm in healthcare applications. The Indonesian Journal of Computer Science, 13(3).

References

[1]. Mallappallil, M., Sabu, J., Gruessner, A., & Salifu, M. (2020). A review of big data and medical research. SAGE open medicine, 8, 2050312120934839.

[2]. Abdulqader, H. A., & Abdulazeez, A. M. (2024). A review on decision tree algorithm in healthcare applications. The Indonesian Journal of Computer Science, 13(3).

[3]. Slocum, M. (2012). Decision making using id3 algorithm. Insight: River Academic J, 8(2), 1-12.

[4]. Di, J., & Xu, Y. (2019). Decision tree improvement algorithm and its application. International Core Journal of Engineering, 5(9), 151-158.

[5]. Tanner, L., Schreiber, M., Low, J. G., Ong, A., Tolfvenstam, T., Lai, Y. L., ... & Ooi, E. E. (2008). Decision tree algorithms predict the diagnosis and outcome of dengue fever in the early phase of illness. PLoS neglected tropical diseases, 2(3), e196.

[6]. abdulaziz Mohsen, A., Alsurori, M., Aldobai, B., & Mohsen, G. A. (2019). New approach to medical diagnosis using artificial neural network and decision tree algorithm: application to dental diseases. International Journal of Information Engineering and Electronic Business, 11(4), 52-60.

[7]. Shouman, M. (2011). Using decision tree for diagnosing heart disease patients.

[8]. Ilyas, H., Ali, S., Ponum, M., Hasan, O., Mahmood, M. T., Iftikhar, M., & Malik, M. H. (2021). Chronic kidney disease diagnosis using decision tree algorithms. BMC nephrology, 22(1), 273.

[9]. Roy, S., Aithal, P. S., & Bose, D. R. (2021). Judging Mental Health Disorders Using Decision Tree Models. International Journal of Health Sciences and Pharmacy (IJHSP), 5(1), 11-22.

[10]. Liu, W. B., Ren, D. Y., Tao, F., & Chen, G. L. (2018). Optimization of medical treatment process and rule mining for hospitalized burn patients based on decision tree. Academic Journal of Second Military Medical University, 39(12), 5-33

[11]. Wu, S. W., Pan, Q., & Chen, T. (2020). Research on diagnosis-related group grouping of inpatient medical expenditure in colorectal cancer patients based on a decision tree model. World Journal of Clinical Cases, 8(12), 10.

[12]. Chanmee, S., & Kesorn, K. (2020). Data quality enhancement for decision tree algorithm using knowledge-based model. Current Applied Science and Technology, 259-277.

[13]. Gulati, P., Sharma, A., & Gupta, M. (2016). Theoretical study of decision tree algorithms to identify pivotal factors for performance improvement: A review. Int. J. Comput. Appl, 141(14), 19-25.

[14]. Abdulqader, H. A., & Abdulazeez, A. M. (2024). A review on decision tree algorithm in healthcare applications. The Indonesian Journal of Computer Science, 13(3).

Cite this article

Xing,J. (2025). A Systematic Review of Decision Trees Application in the Medical Field. Applied and Computational Engineering,202,47-55.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-MLA 2025 Symposium: Intelligent Systems and Automation: AI Models, IoT, and Robotic Algorithms

ISBN: 978-1-80590-497-7(Print) / 978-1-80590-498-4(Online)
Editor: Hisham AbouGrad
Conference website: https://www.confmla.org/london.html
Conference date: 12 November 2025
Series: Applied and Computational Engineering
Volume number: Vol.202
ISSN: 2755-2721(Print) / 2755-273X(Online)

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