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Related Concept Videos

Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
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Related Experiment Video

Updated: Jul 18, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Predicting carcinoid heart disease with the noisy-threshold classifier.

Marcel A J van Gerven1, Rasa Jurgelenaite, Babs G Taal

  • 1Institute for Computing and Information Sciences, Radboud University Nijmegen, Toernooiveld 1, 6525 ED Nijmegen, The Netherlands. marcelge@cs.ru.nl

Artificial Intelligence in Medicine
|November 14, 2006
PubMed
Summary

A novel noisy-threshold classifier accurately predicts carcinoid heart disease (CHD) in neuroendocrine tumor patients. This machine learning approach shows promise for identifying patients at risk of this life-threatening complication.

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Last Updated: Jul 18, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Area of Science:

  • Oncology
  • Biostatistics
  • Machine Learning

Background:

  • Carcinoid heart disease (CHD) is a severe complication of neuroendocrine tumors.
  • Predicting CHD development is crucial for patient management.

Purpose of the Study:

  • To evaluate a novel noisy-threshold classifier for predicting CHD.
  • To compare its performance against other classification algorithms and expert rules.

Main Methods:

  • A dataset of 54 midgut carcinoid tumor patients (22 with CHD) was analyzed.
  • Eleven pre-admission attributes were used for classification.
  • The noisy-threshold classifier was compared with naive-Bayes, logistic regression, C4.5, and a physician's decision rule.

Main Results:

  • The noisy-threshold classifier achieved 72% classification accuracy.
  • Its accuracy was significantly better than logistic regression and C4.5.
  • The area under the ROC curve was 0.66, matching the physician's rule.

Conclusions:

  • The noisy-threshold classifier is a favorable machine learning technique for predicting CHD.
  • It performed comparably to expert physician rules.
  • Its interpretability makes it valuable in complex etiological scenarios.