Cited by 736Artificial Intelligence in HealthcareData Mining Algorithms and ApplicationsData Mining and Machine Learning Applications

The DOI matches its Crossref record; 42 of the 44 references it cites were found in a registry or an index.

Decision Trees: An Overview and Their Use in Medicine

Podgorelec, Vili, Kokol, Peter, Stiglic, Bruno, Rozman, Ivan·2002·Journal of Medical Systems

DOI 10.1023/a:1016409317640

Open this paper in CoChat — free account

Abstract

In medical decision making (classification, diagnosing, etc.) there are many situations where decision must be made effectively and reliably. Conceptual simple decision making models with the possibility of automatic learning are the most appropriate for performing such tasks. Decision trees are a reliable and effective decision making technique that provide high classification accuracy with a simple representation of gathered knowledge and they have been used in different areas of medical decision making. In the paper we present the basic characteristics of decision trees and the successful alternatives to the traditional induction approach with the emphasis on existing and possible future applications in medicine.

Integrity signals

Registry

Crossref · 10.1023/a:1016409317640

Cites

42 of 42 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

A Mathematical Theory of Communication
Shannon, C. E. (1948) · The MIT Press eBooks · doi:10.1002/j.1538-7305.1948.tb00917.x · Cited by 50,387
Optimization by Simulated Annealing
Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983) · Science · doi:10.1126/science.220.4598.671 · Cited by 45,134
Classification and Regression Trees.
Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984) · Biometrics · doi:10.2307/2530946 · Cited by 23,867
Untitled work
(1992) · Cited by 23,431
Induction of decision trees
Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439
Classification and Regression Trees.
Ryzin, J. V., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1986) · Journal of the American Statistical Association · doi:10.2307/2288003 · Cited by 21,030
Genetic Algorithms in Search, Optimization and Machine Learning
Goldberg, D. E., & Robson, D. (1988) · Cited by 18,391
Genetic Algorithms in Search
Goldberg, D. E. (1989) · Medical Entomology and Zoology · Cited by 10,109
The Mathematical Theory of Communication
Shannon, C. E., Weaver, W., & Wiener, N. (1950) · Physics Today · doi:10.1063/1.3067010 · Cited by 9,885
The random subspace method for constructing decision forests
Ho, T. K. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.709601 · Cited by 7,005

Show all 44 references

Research works printed but not linked: 9

Paterson, A., and Niblett, T. B., ACLS Manual, Intelligent Terminals Ltd., Edinburgh, 1982.
Heath, D., Kasif, S., and Salzberg, S., k-DT: A multi-tree learning method. Proc. Second Int. Workshop Multistrategy Learn. pp. 138-149, 1993.
Heath, D., Kasif, S., and Salzberg, S., Learning oblique decision trees. Proc. Thirteenth Int. Joint Conf. Artif. Intellig. (IJCAI-93) pp. 1002-1007, 1993.
Rich, E., and Knight, K., Artificial Intelligence (2nd edn.), McGraw Hill, New York, 1991.
Podgorelec, V., and Kokol, P., Evolutionary decision forests-decision making with multiple evolutionary constructed decision trees, Problems in Applied Mathematics and Computational Intelligence, pp. 97-103, WSES Press, 2001.
Zorman, M., Kokol, P., and Podgorelec, V., Medical decision making supported by hybrid decision trees. Proc. ICSC Symp. Intellig. Syst. Appl. (ISA-2000) ICSC Academic Press, 2000.
Podgorelec, V., and Kokol, P., Induction f medical decision trees with genetic algorithms. Proc. Int. ICSC Congr. Comput. Intellig. Methods Appl. (CIMA 1999) 1999.
Podgorelec, V., Intelligent Systems Design and Knowledge Discovery With Automatic Programming, PhD thesis, University of Maribor, Oct. 2001.
Dantchev, N., Therapeutic decision frees in psychiatry. Encephale-Revue De Psychiatrie Clinique Biologique Et Therapeutique 22(3):205-214, 1996.

Sorted by citations

Items 401–500 of 734

Financial Series Forecasting: A New Fuzzy Inference System for Crisp Values and Interval-Valued Predictions
Alves, K. S. T. R., Ballini, R., & de Aguiar, E. P. (2024) · Computational Economics · doi:10.1007/s10614-024-10670-w · Cited by 7
Data Preparation by CFS: An Essential Approach for Decision Making Using C 4.5 for Medical Data Mining
Ashwinkumar, U. M., & Anandakumar, K. (2013) · International Conference on Advanced Computing and Communication Technologies (ACCT) · doi:10.1109/acct.2013.14 · Cited by 6
Increasing Acceptability of Decision Trees with Domain Attributes Partial Orders
López-Vallverdú, J. A., Riaño, D., & Collado, A. (2007) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms.2007.59 · Cited by 6
Coopetition Effect Determinants: Competitor’s Size, Geographical Scope, Market and Technological Positions
Cygler, J., & Dębkowska, K. (2015) · Organizacija · doi:10.1515/orga-2015-0019 · Cited by 6
Automatic Image Annotation Using Decision Trees and Rough Sets.
Patil, M. P., & Kolhe, S. R. (2014) · Cited by 6
An Application of the Multi Period Decision Trees in the Sustainable Medical Waste Investments
Csorba, L. M., & Crăciun, M. D. (2017) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-62524-9_40 · Cited by 6
Maternal nutrient restriction in early pregnancy increases the risk of late embryo loss despite no effects on peri-implantation interferon-stimulated genes in suckler beef cattle
Serrano, B., Molina, E., Clavé, A. N., Lopez-Helguera, I., Casasús, I., Sanz, A., & Villalba, D. (2019) · Research in Veterinary Science · doi:10.1016/j.rvsc.2019.10.023 · Cited by 6
Best practices for supervised machine learning when examining biomarkers in clinical populations
Schultz, B. G., Joukhadar, Z., Nattala, U., Quiroga, M. D. M., Bolk, F., & Vogel, A. P. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-822884-5.00013-1 · Cited by 7
Risk Assessment of Cardiovascular Diseases Using kNN and Decision Tree Classifier
Jadhav, A. D., & Chobe, S. V. (2022) · Revue d intelligence artificielle · doi:10.18280/ria.360118 · Cited by 6
Rule extraction from decision tree: Transparent expert system of rules
Boruah, A. N., Biswas, S. K., & Bandyopadhyay, S. (2022) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.6935 · Cited by 6
Prediction of radiographic progression pattern in patients with ankylosing spondylitis using group-based trajectory modeling and decision trees
Kang, J., Lee, T.-H., Park, S. Y., Lee, S., Koo, B. S., & Kim, T. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2022.994308 · Cited by 6
Evaluation of changes in glycemic control and diabetic complications over time and factors associated with the progression of diabetic complications in Japanese patients with juvenile‐onset type 1 diabetes mellitus
Masuda, T., Katakami, N., Watanabe, H., Taya, N., Miyashita, K., Takahara, M., Kato, K., Kuroda, A., Matsuhisa, M., & Shimomura, I. (2023) · Journal of Diabetes · doi:10.1111/1753-0407.13486 · Cited by 6
Triage prediction in pediatric patients with respiratory problems
Garmendia, A., Ríos, S. A., López-Guede, J. M., & Graña, M. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.01.122 · Cited by 6
Thrombectomy of Ventricular Assist Device‐Originated Embolic Stroke: A Clinical Decision Model
Colletta, K., Bar, B., Liebo, M., Borys, E., Schneck, M. J., & Gomez, C. R. (2019) · Journal of Neuroimaging · doi:10.1111/jon.12621 · Cited by 6
Smart Trauma: Improving the Delivery of Evidence-Based Trauma Care
Dekonenko, C., McDonald, T. J., & Winfield, R. D. (2019) · Journal of Surgical Research · doi:10.1016/j.jss.2019.04.064 · Cited by 6
Investigating Potential Risk Factors for Cardiovascular Diseases in Adult Qatari Population
Rehman, A. U., Alam, T., & Belhouari, S. B. (2020) · IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT) · doi:10.1109/iciot48696.2020.9089468 · Cited by 6
A novel decision tree approach to predict the probability of conversion to multiple sclerosis in Iranian patients with optic neuritis
Aghdam, K. A., Aghajani, A., Kanani, F., Sanjari, M. S., Chaibakhsh, S., Shirvaniyan, F., Moosavi, D., & Moghaddasi, M. (2020) · Multiple Sclerosis and Related Disorders · doi:10.1016/j.msard.2020.102658 · Cited by 6
Using decision trees to characterize verbal communication during change and stuck episodes in the therapeutic process
MasÃas, V. H., Krause, M., ValdÃ©s, N., PÃ©rez, J. C., & Laengle, S. M. (2015) · Frontiers in Psychology · doi:10.3389/fpsyg.2015.00379 · Cited by 6
Predicting isolated impaired glucose tolerance without oral glucose tolerance test using machine learning in Chinese Han men
Wang, L., Xie, J., Gu, Z., Miao, X., Ma, L., Yan, S., Gong, Y., Li, C., Sun, B., & Ruan, Y. (2025) · Frontiers in Endocrinology · doi:10.3389/fendo.2025.1514397 · Cited by 5
Drug repurposing targeting COVID-19 3CL protease using molecular docking and machine learning regression approaches
Aqeel, I., Majid, A., Albanyan, A., & Wasfi, H. (2025) · Scientific Reports · doi:10.1038/s41598-025-02773-7 · Cited by 5
Using a decision tree algorithm to distinguish between repeated supra-therapeutic and acute acetaminophen exposures
Mehrpour, O., Hoyte, C. O., Nakhaee, S., Mégarbane, B., & Goss, F. R. (2023) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-023-02188-2 · Cited by 5
The Utility of Information Theory Based Methods in the Research of Aging and Longevity
Blokh, D., Gitarts, J., Mizrahi, E. H., Kagansky, N., & Stambler, I. (2023) · Healthy ageing and longevity · doi:10.1007/978-3-031-35176-1_12 · Cited by 6
Refining the rheological characteristics of high drug loading ointment via SDS and machine learning
Qian, X., Wang, K., Ma, Y., Fang, F., Meng, X., Liu, Z., Pan, Y., Zhang, Y., Wang, Y., Wang, X., et al. (2024) · PLoS ONE · doi:10.1371/journal.pone.0303199 · Cited by 5
A novel user scenario and behavior sequence recognition approach based on vision-context fusion architecture
Yuan, W., Chang, D., Mao, C., Wang, L., Ren, K. L., & Han, T. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103161 · Cited by 5
Using Adaptive Learning Platform Data in a Flipped Classroom for Early Detection and Tutoring of Low‐Performing Students
Kaw, A. K., Yalçin, A., & Clark, R. M. (2025) · Computer Applications in Engineering Education · doi:10.1002/cae.70007 · Cited by 5
Predictability of postoperative recurrence on hepatocellular carcinoma through data mining method
Iwahashi, S., Ghaibeh, A. A., Shimada, M., Morine, Y., Imura, S., Ikemoto, T., Saito, Y., & Hirose, J. (2020) · Molecular and Clinical Oncology · doi:10.3892/mco.2020.2116 · Cited by 5
MULTI-OBJECTIVE HYPERPARAMETER TUNING OF CLASSIFIERS FOR DISEASE DIAGNOSIS
Kumar, S., & Ratnoo, S. (2021) · Indian Journal of Computer Science and Engineering · doi:10.21817/indjcse/2021/v12i5/211205081 · Cited by 5
Emergency Medical Assistance by Ambulance Drone Using Machine Learning, Light-Weight Cryptography and Variable Image Steganography
Das, I., Roy, S., Dey, S., Dey, T., & Roy, P. (2022) · IEEE VLSI Device Circuit and System (VLSI DCS) · doi:10.1109/vlsidcs53788.2022.9811471 · Cited by 5
On multivariate randomized classification trees: l0 -based sparsity, VC dimension and decomposition methods
Amaldi, E., Consolo, A., & Manno, A. (2022) · Computers & Operations Research · doi:10.1016/j.cor.2022.106058 · Cited by 6
Validity of Diagnostic Support Model for Attention Deficit Hyperactivity Disorder: A Machine Learning Approach
Chu, K., Huang, H.-J., & Huang, Y. (2023) · Journal of Personalized Medicine · doi:10.3390/jpm13111525 · Cited by 5
Systematic Analysis and Application Prospect of Decision Tree
Xu, J. (2023) · Highlights in Science Engineering and Technology · doi:10.54097/hset.v71i.12687 · Cited by 5
TOMATO PLANT DISEASE CLASSIFICATION USING LOCAL PATTERNS
Agarwal, M. (2023) · Advances in Electrical and Electronic Engineering · doi:10.15598/aeee.v21i4.5036 · Cited by 5
Implementasi Algoritma Decision Tree untuk Rekomendasi Film dan Klasifikasi Rating pada Platform Netflix
Mukhsinin, D. A., Rafliansyah, M., Ibrahim, S. A., Rahmaddeni, R., & Wulandari, D. (2024) · MALCOM Indonesian Journal of Machine Learning and Computer Science · doi:10.57152/malcom.v4i2.1255 · Cited by 5
A spatial mean-based feature fusion and ensemble learning framework for enhanced melanoma detection
Dubey, B., Dwivedi, A., Ghosh, S., & Jana, N. D. (2025) · International Journal of Information Technology · doi:10.1007/s41870-025-02841-1 · Cited by 5
Inducing Decision Trees from Medical Decision Processes
Torres, P., Riaño, D., & López-Vallverdú, J. A. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-18050-7_4 · Cited by 5
Optimization of Sitting Posture Classification based on Anthropometric Data
Martins, L. D. O., Ribeiro, B., de Almeida, R. V., Pereira, H., Jesus, A. P., Quaresma, C., & Vieira, P. M. (2016) · International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC) · doi:10.5220/0005790104060413 · Cited by 5
Generation of structure function based on ambiguous and incompletely specified data using fuzzy random forest
Rabčan, J., Levashenko, V., Zaitseva, E., & Chovancova, O. (2018) · International Conference on Dependable Systems, Services and Technologies (DESSERT) · doi:10.1109/dessert.2018.8409170 · Cited by 5
Categorization of Factors Affecting Classification Algorithms Selection
Reda, M. M., Nassef, M., & Salah, A. (2019) · International Journal of Data Mining & Knowledge Management Process · doi:10.5121/ijdkp.2019.9401 · Cited by 5
Factors Influencing Drug Injection History among Prisoners: A Comparison between Classification and Regression Trees and Logistic Regression Analysis.
Rastegari, A., Haghdoost, A. A., & Baneshi, M. R. (2013) · Europe PMC (PubMed Central) · Cited by 5
Designing Energy-Efficient Decision Tree Memristor Crossbar Circuits Using Binary Classification Graphs
Sinha, P., & Raj, S. (2022) · International Conference on Computer Aided Design · doi:10.1145/3508352.3549448 · Cited by 5
Intelligent diagnosis system for acute respiratory infection in infants
Subiyanto, S., Mulwinda, A., & Andriani, D. (2017) · International Conference on Science in Information Technology · doi:10.1109/icsitech.2017.8257175 · Cited by 5
Assessment of the risk factors in the daily life of stroke patients based on an optimized decision tree
Shao, Z., Chen, C., Li, W., Ren, H., & Chen, W. (2019) · Technology and Health Care · doi:10.3233/thc-199030 · Cited by 5
Malicious URLs and Attachments Detection on Lexical-based Features using Machine Learning Techniques
Zeng, Y. (2018) · UVic’s Research and Learning Repository (University of Victoria) · Cited by 5
A Potential Screening Strategy to Identify Probable Syphilis Infections in the Urban Emergency Department Setting
Hunt, J. H., Laeyendecker, O. B., Rothman, R. E., Fernández, R. E., Dashler, G., Caturegli, P., Hansoti, B., Quinn, T. C., & Hsieh, Y. (2024) · Open Forum Infectious Diseases · doi:10.1093/ofid/ofae207 · Cited by 4
Building Trustworthy AI in Healthcare
Amadi, C., & Ojo, A. (2025) · IEEE Access · doi:10.1109/access.2025.3648410 · Cited by 9
Adaptive feature optimization in deep neural architectures for intelligent diabetes prediction
Abdollahi, J., Nouri-Moghaddam, B., Mohajerzadeh, A., Mirzaei, A., & Derakhshanfard, N. (2026) · Intelligence-Based Medicine · doi:10.1016/j.ibmed.2026.100393 · Cited by 4
Critical review of patient outcome study in head and neck cancer radiotherapy
Chen, J., Yang, Y., Liu, C., Feng, H., Holmes, J., Zhang, L., Frank, S. J., Simone, C. B., Ma, D. J., Patel, S. H., et al. (2025) · Meta-Radiology · doi:10.1016/j.metrad.2025.100151 · Cited by 6
Connecting the dots: How users understand and diagnose smart home ecosystems
Woźniak, M. P., Walczak, A., Grudzień, K., Müller, H., Borchers, J., Koelle, M., & Boll, S. (2025) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2025.103559 · Cited by 4
Artificial Intelligence-Based Methods and Omics for Mental Illness Diagnosis: A Review
de Oliveira, G. S., Rodrigues, F. H. D. S., Pontes, J. G. D. M., & Tasić, L. (2025) · Bioengineering · doi:10.3390/bioengineering12101039 · Cited by 4
Arterial stiffness and biological parameters: A decision tree machine learning application in hypertensive participants
Vallée, A. (2023) · PLoS ONE · doi:10.1371/journal.pone.0288298 · Cited by 4
A Mixed Methods Pilot Study to Evaluate User Engagement with MedMicroMaps: A Novel Interactive E-learning Tool for Medical Microbiology
Harrington, J. (2024) · Medical Science Educator · doi:10.1007/s40670-024-02047-3 · Cited by 4
Intelligent Tutoring System for Medical Students: Opportunities and Challenges
Lamti, S., Malhi, M. E., Sekhsoukh, R., & Kerzazi, N. (2024) · Information systems engineering and management · doi:10.1007/978-3-031-66850-0_27 · Cited by 4
COMPUTATIONAL VISION FOR TOMATO CLASSIFICATION USING A DECISION TREE ALGORITHM
da Fonseca, C. S., Nhantumbo, B. G., Ferreira, Y. M., da Silva, L. A., & Costa, A. G. (2025) · Engenharia Agrícola · doi:10.1590/1809-4430-eng.agric.v45e20240124/2025 · Cited by 4
The influencing factors of depression among Chinese male nursing students: application of decision tree and FsQCA
Wang, Z., Zhao, J., Wang, H., Song, D., Feng, D., Yu, M., & Li, F. (2025) · BMC Nursing · doi:10.1186/s12912-025-03407-z · Cited by 7
Related Risk Factors That Predict Moderate to Severe Asthma Attack in Children: Analysis Based on Logistic Regression and Decision Tree
Li, Q., Fan, Y., Luo, R., Hu, J., Wang, L., & Ai, T. (2025) · International Journal of General Medicine · doi:10.2147/ijgm.s530736 · Cited by 4
A Novel Technique in Classifying Heart Diseases based on Electrocardiogram (ECG) Signals using Deep Learning and Spectrogram Image Analysis
Panganiban, E. B. (2019) · International Journal of Advanced Trends in Computer Science and Engineering · doi:10.30534/ijatcse/2019/102842019 · Cited by 4
Designing an expert system model for pulmonary TB disease diagnose pre-screen laboratory process
Yonas, T., & Ketema, K. (2019) · Journal of Medical Laboratory and Diagnosis · doi:10.5897/jmld2019.0150 · Cited by 4
Artificial Intelligence Uncovered Clinical Factors for Cardiovascular Events in Myocardial Infarction Patients with Glucose Intolerance
Shindo, K., Fukuda, H., Hitsumoto, T., Miyashita, Y., Kim, J., Ito, S., Washio, T., & Kitakaze, M. (2020) · Cardiovascular Drugs and Therapy · doi:10.1007/s10557-020-06987-x · Cited by 4
Analysis and Prediction of Severity of United States Countrywide Car Accidents Based on Machine Learning Techniques
Boyagoda, L. S., & Nawarathna, L. S. (2022) · International Conference on Information Technology Research (ICITR) · doi:10.1109/icitr57877.2022.9993371 · Cited by 4
Behaviour Analysis Using Machine Learning Algorithms In Health Care Sector
Yadav, A., Kumar, D., & Hasija, Y. (2023) · International Conference on Advancement in Computation & Computer Technologies (InCACCT) · doi:10.1109/incacct57535.2023.10141829 · Cited by 4
Analysing influencing factors of accidental oil spills under the IOPC Fund
Çakır, E., & Kamal, B. (2023) · Regional Studies in Marine Science · doi:10.1016/j.rsma.2023.103218 · Cited by 4
Delayed care-seeking in international migrant workers with imported malaria in China
Gu, J., Cao, Y., Chai, L., Xu, E., Liu, K., Chong, Z., Zhang, Y., Zou, D., Xu, Y., Wang, J., et al. (2024) · Journal of Travel Medicine · doi:10.1093/jtm/taae021 · Cited by 4
Comparison of Algorithms for Fuzzy Decision Tree Induction
Rabčan, J., Rusnak, P., Kostolný, J., & Stanković, R. S. (2020) · International Conference on Emerging eLearning Technologies and Applications (ICETA) · doi:10.1109/iceta51985.2020.9379189 · Cited by 4
Fuzzy decision trees as a decision-making framework in the public sector
Benčina, J. (2011) · Yugoslav journal of operations research · doi:10.2298/yjor1102205b · Cited by 4
The challenge of modeling Decision support systems for medical problems using Fuzzy Cognitive Maps: An overview
Groumpos, P. P. (2012) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe.2012.6399662 · Cited by 4
Marketing Data Mining Classifiers: Criteria Selection Issues in Customer Segmentation
Abessi, M., & Yazdi, E. H. (2014) · Cited by 4
An Approach towards Automated Disease Diagnosis & Drug Design Using Hybrid Rough-Decision Tree from Microarray Dataset
Mandal, S. (2013) · Journal of Computer Science & Systems Biology · doi:10.4172/0974-7230.1000130 · Cited by 4
Forecasting delay times in post-exposure prophylaxis to human animal bite injuries in Central Iran: A decision tree analysis
Hamta, A., Saghafipour, A., Hosseinalipour, S. A., & Rezaei, F. (2019) · Veterinary World · doi:10.14202/vetworld.2019.965-971 · Cited by 4
Differentiating patients with radiculopathy from chronic low back pain patients by single surface EMG parameter
Ostojić, S., Peharec, S., Srhoj-Egekher, V., & Cifrek, M. (2018) · Automatika · doi:10.1080/00051144.2018.1553669 · Cited by 4
Anthropometric Measurements for Predicting Low Appendicular Lean Mass Index for the Diagnosis of Sarcopenia: A Machine Learning Model
González-Martin, A. M., Limón-Villegas, E. S., Reyes-Castillo, Z., Esparza-Ros, F., Hernández-Palma, L. A., Santillán-Rivera, M. S., Herrera-Amante, C. A., Ramos-García, C. O., & Righini, N. (2025) · Journal of Functional Morphology and Kinesiology · doi:10.3390/jfmk10030276 · Cited by 4
The Application of Machine Learning in Predicting the Permeability of Drugs Across the Blood Brain Barrier
Jafarpour, S., Asefzadeh, M., & Aboutaleb, E. (2024) · Iranian journal of pharmaceutical research · doi:10.5812/ijpr-149367 · Cited by 3
The future of critical care: AI-powered mortality prediction for acute variceal gastrointestinal bleeding and acute non-variceal gastrointestinal bleeding patients
Liu, Z., Jiang, G., Zhang, L., Shrestha, P., Hu, Y., Zhu, Y., Li, G., Xiong, Y., & Zhan, L. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1580094 · Cited by 5
A Novel Method for Prognostic Risk Classification After Carbon‐Ion Radiotherapy for Hepatocellular Carcinoma Using Data‐Mining Methods
Hayashi, K., Сузукі, О., Ichise, K., Uchida, H., Anzai, M., Hasegawa, A., Shimizu, S., Teshima, T., Fujimoto, J., & Ogawa, K. (2025) · Cancer Science · doi:10.1111/cas.70079 · Cited by 3
Universal cervical‐length screening to prevent preterm birth in twin pregnancy: cost‐utility analysis
Khaikin, Y., Gladstone, R. A., Murphy, K. E., Melamed, N., & Pechlivanoglou, P. (2025) · Ultrasound in Obstetrics and Gynecology · doi:10.1002/uog.29287 · Cited by 4
Machine Learning Models for Differentiating Causes of Dyspnea in Emergency Department Patients: A Retrospective Study
Baarts, J., Leuppi, J. D., Boesing, M., & Lüthi-Corridori, G. (2025) · Journal of Multidisciplinary Healthcare · doi:10.2147/jmdh.s550659 · Cited by 3
An Open-Access Dataset of Hospitalized Cardiac-Arrest Patients: Machine-Learning-Based Predictions Using Clinical Documentation
Rajapaksha, L. T. W., Vidanagamachchi, S., Gunawardena, S., & Thambawita, V. L. (2023) · BioMedInformatics · doi:10.3390/biomedinformatics4010003 · Cited by 3
Can we predict overall survival using machine learning algorithms at 3-months for brain metastases from non-small cell lung cancer after gamma knife radiosurgery?
Moon, H. C., Min, B. J., & Park, Y. S. (2024) · Medicine · doi:10.1097/md.0000000000037084 · Cited by 3
Proximal humeral bone density assessment and prediction analysis using machine learning techniques: An innovative approach in medical research
Li, G., Wu, N., Zhang, J., Song, Y., Ye, T., Zhang, Y., Zhao, D., Yu, P., Wang, L., & Zhuang, C. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e35451 · Cited by 3
The value of combined assessment of tumor cellularity and metabolism by PET/MRI in predicting pathological complete response after neoadjuvant treatment in breast cancer
Ateş, Y. Ü., Aydos, U., Balcı, E., Ateş, S. G., & Atay, L. (2025) · Clinical and translational imaging · doi:10.1007/s40336-025-00690-3 · Cited by 4
Predicting Early Mortality after Thoracic Endovascular Aneurysm Repair: A Machine Learning-Based Decision Tree Analysis
Kano, M., Nishibe, T., Iwasa, T., Matsuda, S., Akiyama, S., Iwahashi, T., Fukuda, S., Shimahara, Y., & Nishibe, M. (2025) · Annals of Vascular Diseases · doi:10.3400/avd.oa.25-00009 · Cited by 3
Towards an intelligent decision-making support in health information systems: Lightweight hybrid data mining class library for object-oriented applications
Castro, J. R., Maldonado, J. A., Garcia, A. A., Castañón–Puga, M., & Figueroa, E. (2016) · 2016 SAI Computing Conference (SAI) · doi:10.1109/sai.2016.7556022 · Cited by 3
Tree analysis modeling of the associations between PHQ-9 depressive symptoms and doctor diagnosis of depression in primary care
Chin, W., Wan, E. Y. F., Dowrick, C. F., Arroll, B., & Lam, C. L. K. (2018) · Psychological Medicine · doi:10.1017/s0033291718001058 · Cited by 3
Web Mining Techniques to Block Spam Web Sites
Esraa, M., F., A., & Hanan, E. (2018) · International Journal of Computer Applications · doi:10.5120/ijca2018917622 · Cited by 3
Supporting Real World Decision Making in Coronary Diseases Using Machine Learning
Kokol, P., Jurman, J., Bogovič, T., Završnik, T., Završnik, J., & Vošner, H. B. (2021) · INQUIRY The Journal of Health Care Organization Provision and Financing · doi:10.1177/0046958021997338 · Cited by 3
AI Approaches for the Prognosis of the Survival (or Not) of Patients with Bone Metastases
Hatzilygeroudis, I., & Prentzas, J. (2021) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai52525.2021.00215 · Cited by 3
Multivariate multi-layer classifier
Zeng, H., & Chen, A. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.108896 · Cited by 3
Predicting adolescents’ suicidal desire using Balanced Random Forest
Ishii, R. (2023) · The Japanese journal of psychology · doi:10.4992/jjpsy.94.22005 · Cited by 3
Decision Tree Analyses for Prediction of QoL over a One-Year Period in Breast Cancer Patients: An Added Value of Patient-Reported Outcomes
Lazarewicz, M. A., Włodarczyk, D., & Reidunsdatter, R. J. (2023) · Cancers · doi:10.3390/cancers15092474 · Cited by 3
Diabetes Prediction using Logistic Regression and Rule Extraction from Decision Tree and Random Forest Classifiers
Bhattacharya, M., & Datta, D. (2023) · International Conference for Emerging Technology (INCET) · doi:10.1109/incet57972.2023.10170270 · Cited by 3
Evaluating ensemble learning techniques for stock index trend prediction: a case of China
Wei, X., Tian, Y., Li, N., & Peng, H. (2023) · Portuguese Economic Journal · doi:10.1007/s10258-023-00246-1 · Cited by 5
Predicting Tumor Dynamics Post-Staged GKRS: Machine Learning Models in Brain Metastases Prognosis
Trofin, A.-M., Buzea, C., Buga, R., Agop, M., Ochiuz, L., Iancu, D. T., & Eva, L. (2024) · Diagnostics · doi:10.3390/diagnostics14121268 · Cited by 3
XDTEncoder: A Deep Explainable Arrhythmia Classification Framework for Smart Healthcare
Wang, J., Lv, J., Li, R., Gong, Y., Chen, Y., Parameshachari, B. D., Słowik, A., & Wei, W. (2024) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3689947 · Cited by 3
Harnessing Machine Learning and Nomogram Models to Aid in Predicting Progression-Free Survival for Gastric Cancer Patients Post-Gastrectomy with Deficient Mismatch Repair(dMMR)
Li, Y., Ma, J., & Cheng, W. (2025) · BMC Cancer · doi:10.1186/s12885-025-13542-0 · Cited by 3
Mapping dominant AI schools to multiple accountability types
Elliott, M. T. J., Deepak, P., & MacCárthaigh, M. (2025) · Transforming Government People Process and Policy · doi:10.1108/tg-11-2024-0272 · Cited by 3
Machine Learning-Assisted Optical Characterization and Growth Modulation of Two-Dimensional Materials
Hu, Z. J., Liu, J., Li, X., Huang, Z., Qi, X., & Du, W. (2025) · Chemistry · doi:10.3390/chemistry7030080 · Cited by 3
Fuzzy Decision Trees in Medical Decision Making Support Systems
Barach, P. R., Levashenko, V., & Zaitseva, E. (2019) · Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care · doi:10.1177/2327857919081009 · Cited by 3
First-line drug resistance profiling of Mycobacterium tuberculosis: a machine learning approach.
Müller, S. J., Meraba, R. L., Dlamini, G. S., & Mapiye, D. S. (2021) · PubMed Central · Cited by 3
EMG vs. Thermography in Severe Carpal Tunnel Syndrome
Papež, B. J., & Palfy, M. (2012) · InTech eBooks · doi:10.5772/26120 · Cited by 3
Understanding Stormwater Pipe Deterioration Through Data Mining
Harvey, R., & McBean, E. A. (2014) · Journal of Water Management Modeling · doi:10.14796/jwmm.c374 · Cited by 3
Expert knowledge integration in the data mining process with application to cardiovascular risk assessment
Tavares, M., Paredes, S., Rocha, T., Carvalho, P., Ramos, J., Mendes, D., Henriques, J. M. O., & Morais, J. (2015) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2015.7318909 · Cited by 3

Authors: Podgorelec, V., Kokol, P., Stiglic, B., & Rozman, I.
Affiliations: University of Maribor (SI)
Corresponding author: Podgorelec, Vili
ORCID: Podgorelec, Vili
Kokol, Peter
Venue: Journal of Medical Systems
Publisher: Springer Science and Business Media LLC
Published: 2002-10
Volume: 26
Issue: 5
Pages: 445-463
ISSN: 0148-5598 (ISSN-L)
1573-689X
DOI: 10.1023/a:1016409317640
OpenAlex: W1584935767
Cited by: 736
References: 44
Citations by year: 2026: 39 · 2025: 83 · 2024: 76 · 2023: 80 · 2022: 62 · 2021: 64 · 2020: 48 · 2019: 46 · 2018: 36 · 2017: 26 · 2016: 21 · 2015: 29 · 2014: 25 · 2013: 24 · 2012: 20
FWCI: 6.93
Type: journal article
Language: en
Open access: closed
Licence: www.springernature.com/gp/researchers/text-and-data-mining (tdm)
www.springernature.com/gp/researchers/text-and-data-mining (vor)
Locations: Journal of Medical Systems (publishedVersion)
PubMed (publishedVersion)
Topics: Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
Keywords: decision tree, clinical decision-making, diagnosis, classification, knowledge representation, inductive learning, machine learning in medicine
MeSH terms: Decision Trees, Humans, United States, Clinical Medicine, Decision Support Systems, Clinical

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Decision Trees: An Overview and Their Use in Medicine

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks