Previous studies that have compared logistic regression (LR), classification and regression tree (CART), and neural networks (NNs) models for their predictive validity have shown inconsistent results in demonstrating superiority of any one model. The three models were tested in a prospective sample of 1225 UK male prisoners followed up for a mean of 3.31 years after release. Items in a widely-used risk assessment instrument (the Historical, Clinical, Risk Management-20, or HCR-20) were used as predictors and violent reconvictions as outcome. Multi-validation procedure was used to reduce sampling error in reporting the predictive accuracy. The low base rate was controlled by using different measures in the three models to minimize prediction error and achieve a more balanced classification. Overall accuracy of the three models varied between 0.59 and 0.67, with an overall AUC range of 0.65–0.72. Although the performance of NNs was slightly better than that of LR and CART models, it did not demonstrate a significant improvement.

http://wrh.scu.edu.cn/UploadFiles/2014-04/0/2014040210191180481.pdf

A Comparison of Logistic Regression, Classification and Regression Tree, and Neural Networks Models in Predicting Violent Re-Offending

Development of three classification trees (CT) based on the CART (Classification and Regression Trees), CHAID (Chi-Square Automatic Interaction Detection) and C45 methodologies for the calculation of probability of hospital mortality; the comparison of the results with the APACHE II, SAPS II and MPM II-24 scores, and with a model based on multiple logistic regression (LR) Retrospective study of 2864 patients Random partition (70:30) into a Development Set (DS) n = 1808 and Validation Set (VS) n = 808 Their properties of discrimination are compared with the ROC curve (AUC CI 95%), Percent of correct classification (PCC CI 95%); and the calibration with the Calibration Curve and the Standardized Mortality Ratio (SMR CI 95%) CTs are produced with a different selection of variables and decision rules: CART (5 variables and 8 decision rules), CHAID (7 variables and 15 rules) and C45 (6 variables and 10 rules) The common variables were: inotropic therapy, Glasgow, age, (A-a)O2 gradient and antecedent of chronic illness In VS: all the models achieved acceptable discrimination with AUC above 07 CT: CART (075(071-081)), CHAID (076(072-079)) and C45 (076(073-080)) PCC: CART (72(69-75)), CHAID (72(69-75)) and C45 (76(73-79)) Calibration (SMR) better in the CT: CART (104(095-131)), CHAID (106(097-115) and C45 (108(098-116)) With different methodologies of CTs, trees are generated with different selection of variables and decision rules The CTs are easy to interpret, and they stratify the risk of hospital mortality The CTs should be taken into account for the classification of the prognosis of critically ill patients

https://link.springer.com/content/pdf/10.1186%2F1471-2288-9-83.pdf

Stratification of the severity of critically ill patients with classification trees

BACKGROUND:The commonly used single tests, based on a 1-time measurement of a physiologic variable, are often poorly predictive of tracheal extubation outcome because they examine only a single aspect of physiological function that affects the extubation outcome. We hypothesized that the constructio

A decision-tree model for predicting extubation outcome in elderly patients after a successful spontaneous breathing trial.

https://rua.ua.es/dspace/bitstream/10045/53439/2/2016_Ruiz-Fernandez_etal_CMPB_accepted.pdf

Aid decision algorithms to estimate the risk in congenital heart surgery

Background

The purpose of this study was to compare the risk factors obtained from a classical statistical method (Cox proportional hazards model) and the results obtained with classification trees (Chi-square Automatic Interaction Detection [CHAID] model) in head and neck squamous cell carcinoma (HNSCC).



Methods

We conducted a retrospective study of 3373 patients with HNSCC and a follow-up longer than 2 years.



Results

The most decisive variable classified by CHAID was T category. N classification was an important prognostic factor in the Cox analysis, but this was not considered in CHAID except in T2 and supraglottic T3. CHAID also indicated that women with oral cavity T3/T4 to N0 tumors had poorer prognosis (28%) than men (58.5%; p value < .001). In oropharynx location, men had lower survival than women (41% vs 72%; p value < .001).



Conclusions

The main benefit of CHAID analysis is that it identifies a relatively small number of patients with a singular behavior, which is more discriminatory for an optimal diagnosis and treatment. © 2012 Wiley Periodicals, Inc. Head Neck, 2012

Prognostic factors in head and neck squamous cell carcinoma: comparison of CHAID decision trees technology and Cox analysis.

https://www.scielosp.org/article/ssm/content/raw/?resource_ssm_path=/media/assets/gs/v22n1/metodologica.pdf

Aproximación a la metodología basada en árboles de decisión (CART). Mortalidad hospitalaria del infarto agudo de miocardio

Aproximación a la metodología basada en árboles de decisión (CART): Mortalidad hospitalaria del infarto agudo de miocardio Approach to the methodology of classification and regression trees

Matilde Palma

Papers

Aproximación a la metodología basada en árboles de decisión (CART). Mortalidad hospitalaria del infarto agudo de miocardio

Aproximación a la metodología basada en árboles de decisión (CART): Mortalidad hospitalaria del infarto agudo de miocardio Approach to the methodology of classification and regression trees