ArticleShanghai archives of psychiatry2015
Decision tree methods: applications for classification and prediction.
Article in Shanghai archives of psychiatry, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 457 papers, 1 of them a synthesis that pooled it.
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Who cites it
457 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- A survey of explainable AI applications in infectious disease diagnosis.BMC infectious diseases · 2026Review
- A Predictive Model for Major Adverse Aortic Events in Patients with Abdominal Aortic Aneurysm Using Clinical and Biomarker Data.International journal of molecular sciences · 2026Article
- Machine learning decision tree models for multiclass classification of prognosis in patient undergoing palliative radiotherapy for bone metastases.Journal of applied clinical medical physics · 2026Article
- Probing Cinnamamides and Benzamides as Anthelmintics: Discovery of Potent Drug-Like Agents Against Angiostrongylus cantonensis.Chemical biology & drug design · 2026Article
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- Comprehensive analysis of the RBP regulome reveals functional modules and drug candidates in liver cancer.Scientific reports · 2026Article
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- Trefoil Factor 3 as a Biomarker for Peripheral Artery Disease.Biomolecules · 2026Article
- Classification of Factors Affecting Manipulation Tendencies Using Decision Trees.Behavioral sciences (Basel, Switzerland) · 2026Article
- Intersectional identity, risk behaviors, and adolescent mental health in South Korea: who suffers the most in the loneliness epidemic?Dialogues in health · 2026Article
- Uncovering Key Characteristics of Antibacterial Peptides through Machine Learning.Macromolecular rapid communications · 2026Article
- Tunable acoustic rotation for deep biophysical phenotyping of preclinical Alzheimer's disease.Materials today. Bio · 2026Article
- Machine learning-enhanced QSAR modeling for predicting drug efficacy against the RET V804M kinase domain mutation.Molecular diversity · 2026Article
- Observational
397 more citing papers are in PubMed but not listed here.
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Authors and funding
2 authors.
Funding
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Abstract
Decision tree methodology is a commonly used data mining method for establishing classification systems based on multiple covariates or for developing prediction algorithms for a target variable. This method classifies a population into branch-like segments that construct an inverted tree with a root node, internal nodes, and leaf nodes. The algorithm is non-parametric and can efficiently deal with large, complicated datasets without imposing a complicated parametric structure. When the sample size is large enough, study data can be divided into training and validation datasets. Using the training dataset to build a decision tree model and a validation dataset to decide on the appropriate tree size needed to achieve the optimal final model. This paper introduces frequently used algorithms used to develop decision trees (including CART, C4.5, CHAID, and QUEST) and describes the SPSS and SAS programs that can be used to visualize tree structure.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.