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PECLIDES Neuro: A Personalisable Clinical Decision Support System for Neurological Diseases

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Methodological Review of Classification Trees for Risk Stratification: An Application Example in the Obesity Paradox

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Bladder cancer in the time of machine learning: Intelligent tools for diagnosis and management

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A multi-objective evolutionary algorithm with decomposition and the information feedback for high-dimensional medical data

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Identifying barriers to participation in cardiac prevention and rehabilitation programmes via decision tree analysis: establishing targets for remedial interventions

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Artificial intelligence and pediatrics: A synthetic mini review

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Multiclassification Prediction of Enzymatic Reactions for Oxidoreductases and Hydrolases Using Reaction Fingerprints and Machine Learning Methods

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Diagnosis of Thyroid Disorders using Decision Tree Splitting Rules

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Performance analysis of Machine learning classifiers for predicting diabetes and prostate cancer

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Non-Parametric Reconstruction of Cosmological Observables Using Gaussian Processes Regression

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Adoption and utilization of medical decision support systems in the diagnosis of febrile Diseases: A systematic literature review

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Predicting post–liver transplant outcomes in patients with acute-on-chronic liver failure using Expert-Augmented Machine Learning

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An efficient improved parrot optimizer for bladder cancer classification

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Evaluating of associated risk factors of metabolic syndrome by using decision tree

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Machine Learning Aided Photonic Diagnostic System for Minimally Invasive Optically Guided Surgery in the Hepatoduodenal Area

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Classification tree analysis of second neoplasms in survivors of childhood cancer

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Indocyanine green clearance as an outcome prediction tool in cardiac surgery: A prospective study

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Index Blood Tests and National Early Warning Scores within 24 Hours of Emergency Admission Can Predict the Risk of In-Hospital Mortality: A Model Development and Validation Study

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Exploring diversity through machine learning: a case for the use of decision trees in social science research

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Decision tree outcome prediction of acute acetaminophen exposure in the United States: A study of 30,000 cases from the National Poison Data System

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Using Explainable AI (XAI) for the Prediction of Falls in the Older Population

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Predictive model for identifying mild cognitive impairment in patients with type 2 diabetes mellitus: A CHAID decision tree analysis

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Efficient bladder cancer diagnosis using an improved RIME algorithm with Orthogonal Learning

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State-of-the-art progress on artificial intelligence and machine learning in accessing molecular coordination and adsorption of corrosion inhibitors

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An enhanced approach for classifying emotions using customized decision tree algorithm

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Application of a decision tree to establish factors associated with a nomogram of aortic stiffness

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Decision Tree-Based Classification for Maintaining Normal Blood Pressure Throughout Early Adulthood and Middle Age: Findings From the Coronary Artery Risk Development in Young Adults (CARDIA) Study

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Prediction of left ventricular ejection fraction changes in heart failure patients using machine learning and electronic health records: a multi-site study

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A Rapid Diagnostic for Detection ofAphelenchoides besseyiandA. fujianensisBased on Real-Time PCR

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Decision Tree Approach to the Impact of Parents’ Oral Health on Dental Caries Experience in Children: A Cross-Sectional Study

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Put Me in the Game Coach! Resident Participation in High-risk Surgery in the Era of Big Data

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Role of patient descriptors in predicting antimicrobial resistance in urinary tract infections using a decision tree approach: A retrospective cohort study

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Credit risk prediction with and without weights of evidence using quantitative learning models

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Utility of support vector machine and decision tree to identify the prognosis of metformin poisoning in the United States: analysis of National Poisoning Data System

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Mostree: Malicious Secure Private Decision Tree Evaluation with Sublinear Communication

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Predicting Rotator Cuff Tears Using Data Mining and Bayesian Likelihood Ratios

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Discriminating Tuberculous Pleural Effusion from Malignant Pleural Effusion Based on Routine Pleural Fluid Biomarkers, Using Mathematical Methods.

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Machine Learning-Based Model for Predicting Incidence and Severity of Acute Ischemic Stroke in Anterior Circulation Large Vessel Occlusion

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Comparison of decision tree with common machine learning models for prediction of biguanide and sulfonylurea poisoning in the United States: an analysis of the National Poison Data System

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Risk factors and safe dosage of levodopa for wearing-off phenomenon in Chinese patients with Parkinson’s disease

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Identifying Behavioral Phenotypes in Chronic Illness: Self-Management of COPD and Comorbid Hypertension

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Quantitative Electroencephalography Analysis for Improved Assessment of Consciousness Levels in Deep Coma Patients Using a Proposed Stimulus Stage

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Trends in Characteristics of CVD in Asia and Japan

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Use of Classification Algorithms in Health Care

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Soft-Voting colorectal cancer risk prediction based on EHLI components

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Optimization of sitting posture classification based on user identification

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A probabilistic framework for short text classification

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Optimizing size selectivity and catch patterns for hake (Merluccius merluccius) and blue whiting (Micromesistius poutassou) by combining square mesh panel and codend designs

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Machine Learning Techniques in Blood Pressure Management During the Acute Phase of Ischemic Stroke

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Automated extraction of expert knowledge in analog topology selection and sizing

McConaghy, T., Palmers, P., Gielen, G. E., & Steyaert, M. S. J. (2008) · doi:10.5555/1509456.1509546 · Cited by 12

Which Is More Useful in Predicting Hospital Mortality -Dichotomised Blood Test Results or Actual Test Values? A Retrospective Study in Two Hospitals

Mohammed, M. A., Rudge, G. M., Wood, G., Smith, G. B., Nangalia, V., Prytherch, D. R., HOLDER, R. L., & Briggs, J. (2012) · PLoS ONE · doi:10.1371/journal.pone.0046860 · Cited by 12

Image and Signal Processing for Networked E-Health Applications

Maglogiannis, I. G., Karpouzis, K. C., & Wallace, M. (2006) · Synthesis lectures on biomedical engineering · doi:10.2200/s00015ed1v01y200602bme002 · Cited by 12

Evaluation of the diagnostic value of joint PET myocardial perfusion and metabolic imaging for vascular stenosis in patients with obstructive coronary artery disease

Wang, F., Xu, W., Lv, W., Du, D., Feng, H., Zhang, X., Wang, S., Chen, W., & Lu, L. (2020) · Journal of Nuclear Cardiology · doi:10.1007/s12350-020-02160-x · Cited by 12

A Bi-criteria Optimization Model for Adjusting the Decision Tree Parameters

Azad, M., & Moshkov, M. (2021) · Kuwait Journal of Science · doi:10.48129/kjs.10725 · Cited by 11

A fully interpretable machine learning model for increasing the effectiveness of urine screening

Ben, F. D., Col, G. D., Cobârzan, D., Turetta, M., Rubin, D., Buttazzi, P., & Antico, A. (2023) · American Journal of Clinical Pathology · doi:10.1093/ajcp/aqad099 · Cited by 11

Software Defect Prediction Using Hybrid Machine Learning Techniques: A Comparative Study

Kumar, H., & Saxena, V. (2024) · Journal of Software Engineering and Applications · doi:10.4236/jsea.2024.174009 · Cited by 11

Application of machine learning tools to study the synergistic impact of physicochemical properties of peptides and filtration membranes on peptide migration during electrodialysis with filtration membranes

Sanchez‐Reinoso, Z., Bazinet, M., Leblanc, B., Clément, J.-P., Germain, P., & Bazinet, L. (2024) · Separation and Purification Technology · doi:10.1016/j.seppur.2024.130877 · Cited by 11

Leveraging Machine Learning for Pediatric Appendicitis Diagnosis: A Retrospective Study Integrating Clinical, Laboratory, and Imaging Data

Navaei, M., Doogchi, Z., Gholami, F., & Tavakoli, M. K. (2025) · Health Science Reports · doi:10.1002/hsr2.70756 · Cited by 11

Classification tree analysis for the discrimination of pleural exudates and transudates

Esquerda, A., Trujillano, J., López‐de‐Ullibarri, I., Bielsa, S., Madroñero, A. B., & Porcel, J. M. (2007) · Clinical Chemistry and Laboratory Medicine (CCLM) · doi:10.1515/cclm.2007.001 · Cited by 11

The use of information theory for the evaluation of biomarkers of aging and physiological age

Blokh, D., & Stambler, I. (2017) · Mechanisms of Ageing and Development · doi:10.1016/j.mad.2017.01.003 · Cited by 11

Blood oxygen level dependent magnetic resonance imaging for detecting pathological patterns in lupus nephritis patients: a preliminary study using a decision tree model

Shi, H., Jia, J., Dong, L., Wei, L., Shang, W., & Zheng, Z. (2018) · BMC Nephrology · doi:10.1186/s12882-017-0787-z · Cited by 11

Treatment outcome clustering patterns correspond to discrete asthma phenotypes in children

Banić, I., Lovrić, M., Cuder, G., Kern, R., Rijavec, M., Korošec, P., & Turkalj, M. (2021) · Asthma Research and Practice · doi:10.1186/s40733-021-00077-x · Cited by 11

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