Decision Trees: An Overview and Their Use in Medicine

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

Electroencephalogram Signals Analysis by Fuzzy Classifiers based on Cumulative Mutual Information.

Rabčan, J., Zaitseva, E., & Levashenko, V. (2018) · IDDM · Cited by 1

Determinants of the magnitude of response to vitamin D supplementation in adolescent girls identified using a decision tree algorithm

Gonoodi, K., Tayefi, M., Bahrami, A., Amirabadizadeh, A., Ferns, G. A., Mohammadi, F., Eslami, S., & Ghayour‐Mobarhan, M. (2019) · BioFactors · doi:10.1002/biof.1540 · Cited by 1

Machine Learning for Health Data Analytics

Iyyanki, M., Jayanthi, P., & Manickam, V. (2019) · Advances in computer and electrical engineering book series · doi:10.4018/978-1-7998-0182-5.ch010 · Cited by 1

Classification Tree with Hybrid Splitting Mechanism

Zeng, H., & Chen, A. (2019) · IEEE International Symposium on Intelligent Systems and Informatics (SISY) · doi:10.1109/sisy47553.2019.9111639 · Cited by 1

Use of AI and Machine Learning for Asthma Patients: A Systematic Literature Review

Fadziso, T. (2020) · Malaysian Journal of Medical and Biological Research · doi:10.18034/mjmbr.v7i2.517 · Cited by 1

Information-Theoretical Analysis of Blood Biomarkers for Age-Related Hip Fracture Risk Evaluation

Blokh, D., Stambler, I., Gitarts, J., Pinco, E., & Mizrahi, E. H. (2021) · DOAJ (DOAJ: Directory of Open Access Journals) · Cited by 1

Prediction of Stroke Risk Factors for Better Pre-emptive Healthcare: A Public-Survey-Based Approach

Banerjee, D., & Singh, J. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-6353-9_2 · Cited by 1

STUDY OF HEART DISEASE PREDICTION USING CNN ALGORITHM

Kumari, D., & Singh, G. (2021) · Journal of Emerging Technologies and Innovative Research · Cited by 1

Prognostic elements extraction from documents to detect prognostic stage

Deshmukh, P. R., & Phalnikar, R. S. (2021) · Computer Methods in Biomechanics & Biomedical Engineering · doi:10.1080/10255842.2021.1955359 · Cited by 1

Expert System for Dengue Fever Prediction (ESDFP)

Boruah, A. N., Biswas, S. K., Baishya, P., & Ealapollu, D. C. (2021) · IEEE International Conference on Technology, Engineering, Management for Societal Impact using Marketing, Entrepreneurship and Talent (TEMSMET) · doi:10.1109/temsmet53515.2021.9768723 · Cited by 1

Computer-Programmed Decision Trees for Assessing Teacher Noticing

Schack, E. O. (2019) · American Educational Research Association Annual Meeting (AERA) · doi:10.3102/1433600 · Cited by 1

Better Decision Tree Induction for Limited Data Sets of Liver Disease

Sug, H. (2012) · Communications in computer and information science · doi:10.1007/978-3-642-35521-9_12 · Cited by 1

Approximating Pandora's Box with CorrelationsPreprint

Chawla, S., Gergatsouli, E., McMahan, J., & Tzamos, C. (2021) · arXiv (Cornell University) · doi:10.4230/lipics.approx/random.2023.26 · Cited by 2

Productivity Prediction in the Upper Wolfcamp Formation Using Weighted Voting Ensemble Machine Learning MethodPreprint

Kim, S., Yoon, H. C., Lim, J.-T., & Kim, K. H. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4166149 · Cited by 1

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Cornejo-Bueno, L. M., Pérez‐Aracil, J., Casanova-Mateo, C., Sanz, J., & Salcedo‐Sanz, S. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4352177 · Cited by 2

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Malviya, R., Rajput, S., & Vaidya, M. (2024) · Wiley eBooks · doi:10.1002/9781394230914.ch9 · Cited by 2

Risk Factors and Predictive Modeling for Aortic Aneurysm

Vanichbuncha, T. (2012) · KTH Publication Database DiVA (KTH Royal Institute of Technology)

An automatized case-based cognitive skills assessment approach

Petrović, J., Tralić, D., & Pale, P. (2013) · International Symposium ELMAR (ELMAR)

The Exposition of Fuzzy Decision Trees and Their Application in Biology

Beynon, M. J., & Park, K. J. (2009) · IGI Global eBooks · doi:10.4018/978-1-59904-996-0.ch020

Female breast cancer late presentation in Saudi Arabia: a mixed methods study

Alhurishi, S. A. (2014) · White Rose eTheses Online (University of Leeds, The University of Sheffield, University of York)

Relation quantitative entre les facteurs de risque individuels/professionnels et la fréquence de la douleur lombaire aiguë par une approche non paramétrique

Mendelek, F., Kheir, R. B., Caby, I., Thévenon, A., & Pelayo, P. (2011) · Revue du Rhumatisme · doi:10.1016/j.rhum.2011.01.015

Formal Decision Trees in Plastic Surgery

Lloyd, M. S., Parikh, A., Clarke, A. M., Withey, S., Carson, E. R., Simmons, J., McEvoy, H., & Butler, P. E. M. (2005) · Plastic & Reconstructive Surgery · doi:10.1097/01.prs.0000179356.79256.a7

Modelling traditional Chinese medicine therapy planning with POMDP

Feng, Q. B., Zhou, X., Huang, H., Zhang, X., & Zhang, R. (2013) · International Journal of Functional Informatics and Personalised Medicine · doi:10.1504/ijfipm.2013.057405

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TAUT, D. A. S., & Sitar-Tăut, A. (2010) · RePEc: Research Papers in Economics

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McCallie, D. P. (2015) · Health informatics · doi:10.1007/978-3-319-20765-0_28

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Njoroge, A. M. (2013)

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Hussein, N. A. (2014) · Eastern Mediterranean University Institutional Repository (Eastern Mediterranean University)

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Parameswari, D. T. (2013) · International Journal of Innovative Research in Computer and Communication Engineering

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Podgorelec, V. (2009)

Artificial intelligence applied to medical genetics

Braaten, Ø. (2011) · NORA - Norwegian Open Research Archives

Ant-Inspired Algorithms for Decision Tree Induction - An Evaluation on Biomedical Signals.

Burša, M., & Lhotská, L. (2015)

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Park, Y., Ho, J. C., & Ghosh, J. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.05325

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Cerrito, P. B., & Cerrito, J. (2010) · Advances in medical technologies and clinical practice book series · doi:10.4018/978-1-61520-905-7.ch013

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Gudelis, M. (2014) · Repositori ObertUDL (University of Lleida)

Topics in Evidence Synthesis

Pozzi, L. (2014) · eScholarship (California Digital Library)

Computational approach to detect the culprit genes responsible for a Disease

War, D. K., & Saha, G. K. (2015) · International Symposium on Advanced Computing and Communication (ISACC) · doi:10.1109/isacc.2015.7377311

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Yang, S. (2016) · Lu Zone Ul (Laurentian University)

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Balcan, M.-F., Blum, A. L., & Nagarajan, V. (2019) · Theoretical Computer Science · doi:10.1016/j.tcs.2019.11.010

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Faizin, M. M., Subiyanto, S., & Arief, U. M. (2017) · doi:10.1109/iceeie.2017.8328772

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Eigner, I., & Hamper, A. (2018) · Healthcare delivery in the information age · doi:10.1007/978-3-319-72287-0_5

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Morani, K., Eigner, G., Ferenci, T., Kovács, L., & Engin, Ş. N. (2018) · IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI) · doi:10.1109/saci.2018.8440931

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Júnior, M. S., Gaspar, J., Reis, Z. S. N., & Nobre, C. N. (2018) · doi:10.5753/sbcas.2018.3695

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Delgado, E. C. C., & González, I. I. C. (2016) · IGI Global eBooks · doi:10.4018/978-1-4666-9978-6.ch045

Heart diseases diagnosis using artificial neural networks

Alsalamah, M. (2017) · Coventry University Open Collections (Coventry university)

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Choudhury, D. K. (2019) · Metamorphosis · doi:10.1177/0972622519872653

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Fathalla, K. M. (2019) · Aston Publications Explorer (Aston University) · doi:10.48780/publications.aston.ac.uk.00041178

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Gupta, A., & Agarwal, A. (2020) · Joint International Conference on Data Science & Management of Data (CODS-COMAD) · doi:10.1145/3430984.3430986

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Mugford, R. (2016) · doi:10.22215/etd/2016-11258

Construction of diagnostic expert-medical system using neural networks

Glybovets, M., Salata, K. V., & Ткач, Н. А. (2020) · PROBLEMS IN PROGRAMMING · doi:10.15407/pp2020.02-03.384

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Nikumbh, D. D., Sayyad, S., Joshi, R. R., Dubey, K. S., Mehta, D. V., & Matta, D. K. (2021) · Advances in healthcare information systems and administration book series · doi:10.4018/978-1-7998-7709-7.ch004

Junction design rules

Bezembinder, E. M. (2021) · doi:10.3990/1.9789036552448

IT Decision Support System and Modeling of Medical Processes

Stachlewski, T., & Šajbidor, M. (2022) · Studies in systems, decision and control · doi:10.1007/978-3-030-95813-8_16

Machine Learning for Health Data Analytics

Iyyanki, M., Jayanthi, P., & Manickam, V. (2022) · IGI Global eBooks · doi:10.4018/978-1-6684-6291-1.ch054

Clinical Decision Support Systems for Remote and Commuting Clinicians

Maglogiannis, I. G., Karpouzis, K. C., & Wallace, M. (2006) · Synthesis lectures on biomedical engineering · doi:10.1007/978-3-031-01609-7_4

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Belciug, S., & Iliescu, D. G. (2022) · Intelligent systems reference library · doi:10.1007/978-3-031-18154-2_3

A multi-attribute utility framework for patients to determine childbirth method considering uncertainties, patient preferences, risk attitudes, and pregnancy complications

Aziz, R. A., & Zhuang, J. (2022) · IISE Transactions on Healthcare Systems Engineering · doi:10.1080/24725579.2022.2149637 · Cited by 1

Fuzzy Cognitive Strategic Map Hierarchies In Performance Measurement, Modeling And Reorganization

Kouroupaki, I., Mpafas, C., & Glykas, M. (2022) · The European Proceedings of Social & Behavioural Sciences · doi:10.15405/epsbs.2022.12.02.18

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Valero-Leal, E., Campos, M., & Juárez, J. M. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-23618-1_29

Machine learning-based identification of key lipid metabolism-related genes and associated ceRNA networks in NAFLDPreprint

Fan, X., Qin, R., Zhu, Y., Huang, W., Su, Y., Zhang, Y., Wang, L., & Wang, H. (2023) · Research Square · doi:10.21203/rs.3.rs-2869422/v1

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Huţanu, A., Molnar, A. A., Pal, K., Gabor, M. R., Szederjesi, J., & Dobreanu, M. (2023) · Revista română de medicină de laborator · doi:10.2478/rrlm-2023-0015

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Jiang, Y., Yu, H., & Fu, X. (2023) · Communications in computer and information science · doi:10.1007/978-981-99-4826-0_10

iLEC-DNA: Identifying Long Extra-chromosomal Circular DNA by Fusing Sequence-derived Features of Physicochemical Properties and Nucleotide Distribution PatternsPreprint

Abbasi, A. F., Asim, M. N., Dengel, A. R., & Ahmed, S. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.09.01.555875

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Ozaltun, E. A., Moghaddamnia, S., & Habiboglu, M. G. (2023) · AIP conference proceedings · doi:10.1063/12.0023974

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Panjaburee, P., Srisawasdi, N., & Poompimol, S. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-49212-9_10

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Amanat, M., & Singh, R. (2024) · Bangladesh Journal of Medical Science · doi:10.3329/bjms.v23i2.72092

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Su, J., Tang, Y., Wang, Y., Chen, C., & Song, B. (2024) · Research Square · doi:10.21203/rs.3.rs-4385977/v1

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Jensen, J., Waldrop, J. B., Derouin, A. L., & Siegel, A. (2024) · Journal of the Association of Nurses in AIDS Care · doi:10.1097/jnc.0000000000000462

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Camici, M., Gottardelli, B., Novellino, T., Masciocchi, C., Lamonica, S., & Murri, R. (2024) · American Journal of Infection Control · doi:10.1016/j.ajic.2024.07.011

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Xiao, J. (2024) · Springer series in biomaterials science and engineering · doi:10.1007/978-981-10-1941-8_3

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Al-Ramini, A., Fallahtafti, F., Takallou, M. A., Pipinos, I. I., Myers, S. A., & Alsaleem, F. (2024) · medRxiv · doi:10.1101/2024.08.05.24311525

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Tsabedze, M., Mpinda, B. N., & Awe, O. O. (2024) · STEAM-H: Science, Technology, Engineering, Agriculture, Mathematics & Health · doi:10.1007/978-3-031-72215-8_22

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Puentes, P. R., Claros, N. A., & Arbeláez, P. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-15393-8.00003-8

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Binokay, H., & Sertdemir, Y. (2025) · Scientific reports in medicine. · doi:10.37609/srinmed.25

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Borkakoty, S., Islam, A. U., & Bora, K. C. (2025) · Studies in computational intelligence · doi:10.1007/978-3-031-80813-5_11

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Das, I., Bhattacharya, A., Bhowmick, A., Das, D., Chanda, M., & Debnath, P. (2025) · AI-Driven Smart Healthcare for Society 5.0 · doi:10.1109/ieeeconf64992.2025.10963054

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Niu, G. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-23663-1.00013-6

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anon, M., Kaosar, M. G., & Abu-Khalaf, J. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5412881

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Verde, L., de Biase, M. S., D’Angelo, G., Petruolo, R., Tirelli, P., Guarino, S., Sessa, A. D., Marzuillo, P., & Marrone, S. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.10.027

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Felnhofer, A., Yeghiazaryan, L., Piribauer, F., & Fiala, C. (2025) · Perspectives on Sexual and Reproductive Health · doi:10.1111/psrh.70045

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Duan, J., Zhao, Y., Shi, Q.-H., Wang, Y., Huang, X., Rao, Q., Fan, S., & Xu, J. (2025) · BMC Gastroenterology · doi:10.1186/s12876-025-04411-y

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da Silva, E. M., de Campos, C. M., de Godoy, C. G., de Oliveira, D. B., Alonso, A. C., da Silva, V. C., Schmitt, A. C. B., Ochiai, G. S., Viana, B. D. O. C., da Silva, J. M., et al. (2025) · Clinics · doi:10.1016/j.clinsp.2025.100846

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Hirosawa, T. (2025) · Springer eBooks · doi:10.1007/978-981-95-4338-0_8

Bibliography

(2025) · Elsevier eBooks · doi:10.1016/b978-0-443-40300-2.00014-x

Additional file 2 of Demographic and clinical characteristics associated with advanced stage colorectal cancer: a registry-based cohort study in Saudi Arabia

Alsadhan, N. M., Alhurishi, S. A., Pujades-Rodriguez, M., Shuweihdi, F., Brennan, C., & West, R. M. (2024) · Figshare · doi:10.6084/m9.figshare.26713088

Additional file 1 of Histopathological distinction of non-invasive and invasive bladder cancers using machine learning approaches

Yin, P.-N., Kishan, K. C., Wei, S., Yu, Q., Li, R., Haake, A. R., Miyamoto, H., & Cui, F. (2020) · Figshare · doi:10.6084/m9.figshare.12671171

Additional file 1 of Discovery of novel JAK1 inhibitors through combining machine learning, structure-based pharmacophore modeling and bio-evaluation

Wang, Z., Sun, L., Xu, Y., Liang, P., Xu, K., & Huang, J. (2024) · Figshare · doi:10.6084/m9.figshare.26613380

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

The Limitations of Decision Trees and Automatic Learning in Real World Medical Decision Making

Kokol, P., Zorman, M., Štiglic, M. M., & Malèiae, I. (1998) · Studies in health technology and informatics · doi:10.3233/978-1-60750-896-0-529 · Cited by 3

Árboles de decisión como herramienta en el diagnóstico médico

Barrientos-Martínez, R.-E., Ramírez, N. C., Mesa, H., Suárez, I. R., Gogeascoechea-Trejo, M. D. C., Pavón-León, P., & Morales, S. L. B. (2009) · Revista Médica de la Universidad Veracruzana · Cited by 12

Inductive Learning as a Method for Medical Decision Making

Deželić, G., & Kern, J. (1991) · Lecture notes in medical informatics · doi:10.1007/978-3-642-93503-9_58 · Cited by 1

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