Predicting carcinoid heart disease with the noisy-threshold classifier

Summary Objective To predict the development of carcinoid heart disease (CHD), which is a life-threatening complication of certain neuroendocrine tumors. To this end, a novel type of Bayesian classifier, known as the noisy-threshold classifier, is applied. Materials and methods Fifty-four cases of p...

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Bibliographic Details
Published in:Artificial intelligence in medicine 2007-05, Vol.40 (1), p.45-55
Main Authors: van Gerven, Marcel A.J, Jurgelenaite, Rasa, Taal, Babs G, Heskes, Tom, Lucas, Peter J.F
Format: Article
Language:English
Subjects:
Algorithms
Area Under Curve
Artificial Intelligence
Bayes Theorem
Bayesian classification
Carcinoid heart disease
Carcinoid Heart Disease - diagnosis
Causal independence
Decision Support Techniques
Decision Trees
Diagnosis, Computer-Assisted
Humans
Internal Medicine
Logistic Models
Models, Statistical
Noisy-threshold model
Other
Predictive Value of Tests
Prognosis
ROC Curve
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Summary:Summary Objective To predict the development of carcinoid heart disease (CHD), which is a life-threatening complication of certain neuroendocrine tumors. To this end, a novel type of Bayesian classifier, known as the noisy-threshold classifier, is applied. Materials and methods Fifty-four cases of patients that suffered from a low-grade midgut carcinoid tumor, of which 22 patients developed CHD, were obtained from the Netherlands Cancer Institute (NKI). Eleven attributes that are known at admission have been used to classify whether the patient develops CHD. Classification accuracy and area under the receiver operating characteristics (ROC) curve of the noisy-threshold classifier are compared with those of the naive-Bayes classifier, logistic regression, the decision-tree learning algorithm C4.5, and a decision rule, as formulated by an expert physician. Results The noisy-threshold classifier showed the best classification accuracy of 72% correctly classified cases, although differences were significant only for logistic regression and C4.5. An area under the ROC curve of 0.66 was attained for the noisy-threshold classifier, and equaled that of the physician’s decision-rule. Conclusions The noisy-threshold classifier performed favorably to other state-of-the-art classification algorithms, and equally well as a decision-rule that was formulated by the physician. Furthermore, the semantics of the noisy-threshold classifier make it a useful machine learning technique in domains where multiple causes influence a common effect.
ISSN:0933-3657
1873-2860
DOI:10.1016/j.artmed.2006.09.003