Classification and Regression Trees: A Possible Method for Creating Risk Groups for Progression to Diabetic Nephropathy

Sayyad, Mehmood G; Gopal, G.; Shahani, A. K.

doi:10.3923/jas.2011.2076.2083

Cited by 5 publications

(6 citation statements)

References 18 publications

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“…, Sayyad et al . ), thus also avoiding the use of an independent data set. For these studies, usually conducted with smaller sample sizes, rather than lose a portion of the sample to training and testing, randomly selected samples of the same data set were retested several times to observe for consistency of the tree models.…”

Section: Discussionmentioning

confidence: 99%

“…Sayyad et al . (), for instance, performed cross‐validation with 10 randomly selected subsets (called ‘sample folds’), providing a measure of the final tree's predictive accuracy for risk of progression of diabetic nephropathy. This type of validation technique is open to criticism for not testing the model on observations quarantined from the model during its development.…”

Section: Discussionmentioning

confidence: 99%

“…Other researchers describe using a 10-fold cross-validation methodology for their medical research (Fan et al 2006, Frisman et al 2008, Protopopoff et al 2009, Sayyad et al 2011, thus also avoiding the use of an independent data set. For these studies, usually conducted with smaller sample sizes, rather than lose a portion of the sample to training and testing, randomly selected samples of the same data set were retested several times to observe for consistency of the tree models.…”

Section: Validation In Classification and Regression Tree Analysismentioning

confidence: 99%

“…For these studies, usually conducted with smaller sample sizes, rather than lose a portion of the sample to training and testing, randomly selected samples of the same data set were retested several times to observe for consistency of the tree models. Sayyad et al (2011), for instance, performed cross-validation with 10 randomly selected subsets (called 'sample folds'), providing a measure of the final tree's predictive accuracy for risk of progression of diabetic nephropathy. This type of validation technique is open to criticism for not testing the model on observations quarantined from the model during its development.…”

Section: Validation In Classification and Regression Tree Analysismentioning

confidence: 99%

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The process and utility of classification and regression tree methodology in nursing research

Kühn

Page

Ward

et al. 2013

Journal of Advanced Nursing

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AimThis paper presents a discussion of classification and regression tree analysis and its utility in nursing research.BackgroundClassification and regression tree analysis is an exploratory research method used to illustrate associations between variables not suited to traditional regression analysis. Complex interactions are demonstrated between covariates and variables of interest in inverted tree diagrams.DesignDiscussion paper.Data sourcesEnglish language literature was sourced from eBooks, Medline Complete and CINAHL Plus databases, Google and Google Scholar, hard copy research texts and retrieved reference lists for terms including classification and regression tree* and derivatives and recursive partitioning from 1984–2013.DiscussionClassification and regression tree analysis is an important method used to identify previously unknown patterns amongst data. Whilst there are several reasons to embrace this method as a means of exploratory quantitative research, issues regarding quality of data as well as the usefulness and validity of the findings should be considered.Implications for Nursing ResearchClassification and regression tree analysis is a valuable tool to guide nurses to reduce gaps in the application of evidence to practice. With the ever-expanding availability of data, it is important that nurses understand the utility and limitations of the research method.ConclusionClassification and regression tree analysis is an easily interpreted method for modelling interactions between health-related variables that would otherwise remain obscured. Knowledge is presented graphically, providing insightful understanding of complex and hierarchical relationships in an accessible and useful way to nursing and other health professions.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Validation In Classification and Regression Tree Analysismentioning

confidence: 99%

Section: Validation In Classification and Regression Tree Analysismentioning

confidence: 99%

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The process and utility of classification and regression tree methodology in nursing research

Kühn

Page

Ward

et al. 2013

Journal of Advanced Nursing

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“…Gini index could be computed using formula given below. In formula 'a' is each attribute in given instance, F1 and F2 represents subset of instances where each subset belongs to a category of 'a', c represents the classes in target variable, Pk is probability that F belongs to class k. [31]…”

Section: Cartmentioning

confidence: 99%

Evolving A Neural Network to Predict Diabetic Neuropathy

Reddy

Mahesh

Nanjundan³

2018

ICST Transactions on Scalable Information Systems

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One of the main areas where machine learning (ML) techniques are used vastly is in prediction of diseases. Diabetic neuropathy (DN) disease is a complication of diabetes which causes damage to nerves. Early prediction of DN helps diabetic patient to avoid its complications. The main aim of this work is to identify various risk factors of DN and predict it accurately using ML techniques. Radial basis function (RBF) network is an artificial neural network proposed to obtain better results than traditional ML classification techniques. CART, random forest and logistic regression are existing classification techniques considered. Accuracy, recall, f1 score, area under ROC curve (AUC), Matthews correlation coefficient (MCC) and kolmogorov-smirnov (KS) statistic are performance metrics used to evaluate and compare algorithms. From comparative study it was observed that proposed technique RBF network performed better. The performance metric values obtained for RBF network are accuracy-68.18%, recall-0.909, f1score-0.7407, AUC-0.6405, MCC-0.4082 and KS statistic-0.5417. Accordingly, the use of RBF network while predicting DN gives accurate and better results.

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Classification and Regression Tree and Computer Adaptive Testing in Cardiac Rehabilitation: Instrument Validation Study

2020

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Background There is a need for shorter-length assessments that capture patient questionnaire data while attaining high data quality without an undue response burden on patients. Computerized adaptive testing (CAT) and classification and regression tree (CART) methods have the potential to meet these needs and can offer attractive options to shorten questionnaire lengths. Objective The objective of this study was to test whether CAT or CART was best suited to reduce the number of questionnaire items in multiple domains (eg, anxiety, depression, quality of life, and social support) used for a needs assessment procedure (NAP) within the field of cardiac rehabilitation (CR) without the loss of data quality. Methods NAP data of 2837 CR patients from a multicenter Cardiac Rehabilitation Decision Support System (CARDSS) Web-based program was used. Patients used a Web-based portal, MyCARDSS, to provide their data. CAT and CART were assessed based on their performances in shortening the NAP procedure and in terms of sensitivity and specificity. Results With CAT and CART, an overall reduction of 36% and 72% of NAP questionnaire length, respectively, was achieved, with a mean sensitivity and specificity of 0.765 and 0.817 for CAT, 0.777 and 0.877 for classification trees, and 0.743 and 0.40 for regression trees, respectively. Conclusions Both CAT and CART can be used to shorten the questionnaires of the NAP used within the field of CR. CART, however, showed the best performance, with a twice as large overall decrease in the number of questionnaire items of the NAP compared to CAT and the highest sensitivity and specificity. To our knowledge, our study is the first to assess the differences in performance between CAT and CART for shortening questionnaire lengths. Future research should consider administering varied assessments of patients over time to monitor their progress in multiple domains. For CR professionals, CART integrated with MyCARDSS would provide a feedback loop that informs the rehabilitation progress of their patients by providing real-time patient measurements.

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Classification and Regression Trees: A Possible Method for Creating Risk Groups for Progression to Diabetic Nephropathy

Cited by 5 publications

References 18 publications

The process and utility of classification and regression tree methodology in nursing research

The process and utility of classification and regression tree methodology in nursing research

Evolving A Neural Network to Predict Diabetic Neuropathy

Classification and Regression Tree and Computer Adaptive Testing in Cardiac Rehabilitation: Instrument Validation Study

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