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Podgorelec, Vili; Kokol, Peter; Stiglic, Bruno; Rozman, Ivan

doi:10.1023/a:1016409317640

Cited by 532 publications

(156 citation statements)

References 7 publications

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“…In fact, decision trees are reliable and effective decision making techniques used in different areas of medical decision making [45]. Meanwhile, in evidence-based medicine, it was used for clinical decision analysis [46].…”

Section: Methodsmentioning

confidence: 99%

A Hybrid Computer-aided-diagnosis System for Prediction of Breast Cancer Recurrence (HPBCR) Using Optimized Ensemble Learning

Mohebian

Marateb

Mansourian

et al. 2017

Computational and Structural Biotechnology Journal

130

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Cancer is a collection of diseases that involves growing abnormal cells with the potential to invade or spread to the body. Breast cancer is the second leading cause of cancer death among women. A method for 5-year breast cancer recurrence prediction is presented in this manuscript. Clinicopathologic characteristics of 579 breast cancer patients (recurrence prevalence of 19.3%) were analyzed and discriminative features were selected using statistical feature selection methods. They were further refined by Particle Swarm Optimization (PSO) as the inputs of the classification system with ensemble learning (Bagged Decision Tree: BDT). The proper combination of selected categorical features and also the weight (importance) of the selected interval-measurement-scale features were identified by the PSO algorithm. The performance of HPBCR (hybrid predictor of breast cancer recurrence) was assessed using the holdout and 4-fold cross-validation. Three other classifiers namely as supported vector machines, DT, and multilayer perceptron neural network were used for comparison. The selected features were diagnosis age, tumor size, lymph node involvement ratio, number of involved axillary lymph nodes, progesterone receptor expression, having hormone therapy and type of surgery. The minimum sensitivity, specificity, precision and accuracy of HPBCR were 77%, 93%, 95% and 85%, respectively in the entire cross-validation folds and the hold-out test fold. HPBCR outperformed the other tested classifiers. It showed excellent agreement with the gold standard (i.e. the oncologist opinion after blood tumor marker and imaging tests, and tissue biopsy). This algorithm is thus a promising online tool for the prediction of breast cancer recurrence.

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Section: Methodsmentioning

confidence: 99%

A Hybrid Computer-aided-diagnosis System for Prediction of Breast Cancer Recurrence (HPBCR) Using Optimized Ensemble Learning

Mohebian

Marateb

Mansourian

et al. 2017

Computational and Structural Biotechnology Journal

130

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“…It has proven to be reliable and effective, providing high classification accuracy with a simple representation of gathered knowledge. Because of its tree structure, it can be readily interpreted and therefore more likely to be adopted than, say, an ambiguous numerical risk score 26 . This study employed the decision tree algorithm in combination with over-sampling and feature selection techniques to identify and represent the nonlinear interactions between preoperative variables.…”

Section: Introductionmentioning

confidence: 99%

Decision tree for adjuvant right ventricular support in patients receiving a left ventricular assist device

Wang

Simon

Bonde

et al. 2012

The Journal of Heart and Lung Transplantation

117

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Background Right ventricular (RV) failure is a significant complication following implantation of a left ventricular assist device (LVAD). It is therefore important to identify patients at risk a-priori. However, prognostic models derived from multivariate analyses have had limited predictive power. Methods This study retrospectively analyzed 183 patient records of LVAD recipients between May 1996 and Oct. 2009; 27 of which later required a right ventricular assist device (RVAD+) and 156 remained on LVAD only (RVAD−) until the time of transplantation or death. A decision tree model was constructed to represent combinatorial nonlinear relationships of the preoperative data that are predictive of the need for RVAD support. Results An optimal set of eight preoperative variables were identified: transpulmonary gradient, age, right atrial pressure, international normalized ratio, heart rate, white blood cell count, alanine aminotransferase and the number of inotropic agents. The resultant decision tree, comprised of 28 branches and 14 leaves, identified RVAD+ patients with 85% sensitivity, RVAD− patients with 83% specificity, and exhibited an area under the ROC curve of 0.87. Conclusions The decision tree model developed in this study exhibited several advantages over existing risk scores. Quantitatively, it provided improved prognosis of RV support by encoding the nonlinear, synergic interactions among preoperative variables. Because of its intuitive structure, it more closely mimics clinical reasoning and therefore can be more readily interpreted. Further development with additional multi-center, longitudinal data may provide a valuable prognostic tool for triage of LVAD therapy, and potentially improve outcomes.

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“…To the best of our knowledge, this is the first study conducted using Florida data to identify subgroups that share the same patterns of characteristics in terms of not being up to date with CRC screening. Studies indicate that CTA is a powerful decision-making tool (29) and a promising strategy to tailor interventions to population subgroups at high risk (30). Compared with cluster analysis or logistic regression analysis, the visual image of a hierarchical tree structure provides benefit to CRC practitioners, researchers, community partners and policy makers who are involved in deciding the priority populations in which to improve CRC screening rates.…”

Section: Discussionmentioning

confidence: 99%

Florida Populations Most at Risk of Not Being Up to Date With Colorectal Cancer Screening

et al. 2018

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IntroductionThe purpose of this study was to examine the characteristics of populations at risk of not being up to date on colorectal cancer screening in Florida.MethodsWe used Exhaustive Chi-squared Automatic Interaction Detection, a classification tree analysis, to identify subgroups not up to date with colorectal cancer screening using the 2013 Florida Behavioral Risk Factor Surveillance System. The data set was restricted to adults aged 50 to 75 years (n = 14,756).ResultsOnly 65.5% of the sample was up to date on colorectal cancer screening. Having no insurance and having a primary care provider were the most significant predictors of not being up to date on screening. The highest risk subgroups were 1) respondents with no insurance and no primary care provider, regardless of their employment status (screening rate, 12.1%–23.7%); 2) respondents with no insurance but had a primary care provider and were employed (screening rate, 32.3%); and 3) respondents with insurance, who were younger than 55 years, and who were current smokers (screening rate, 42.0%).ConclusionSome populations in Florida are at high risk for not being up to date on colorectal cancer screening. To achieve Healthy People 2020 goals, interventions may need to be further tailored to target these subgroups.

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Cited by 532 publications

References 7 publications

A Hybrid Computer-aided-diagnosis System for Prediction of Breast Cancer Recurrence (HPBCR) Using Optimized Ensemble Learning

A Hybrid Computer-aided-diagnosis System for Prediction of Breast Cancer Recurrence (HPBCR) Using Optimized Ensemble Learning

Decision tree for adjuvant right ventricular support in patients receiving a left ventricular assist device

Florida Populations Most at Risk of Not Being Up to Date With Colorectal Cancer Screening

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