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The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under both the...
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Related Experiment Video

Updated: Jun 6, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Reliable confidence measures for medical diagnosis with evolutionary algorithms.

Antonis Lambrou1, Harris Papadopoulos, Alex Gammerman

  • 1Computer Learning Research Centre and the Department of Computer Science, Royal Holloway, University of London, Surrey, UK. A.Lambrou@cs.rhul.ac.uk

IEEE Transactions on Information Technology in Biomedicine : a Publication of the IEEE Engineering in Medicine and Biology Society
|November 11, 2010
PubMed
Summary

This study introduces a novel Conformal Predictor (CP) using a genetic algorithm (GA) for medical diagnosis. The method provides accurate predictions with reliable confidence measures for breast and ovarian cancer detection.

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Published on: October 11, 2018

Area of Science:

  • Machine Learning
  • Medical Diagnostics
  • Bioinformatics

Background:

  • Conformal Predictors (CPs) offer valuable confidence measures for machine-generated medical diagnoses.
  • Risk assessment in medical predictions is crucial for patient outcomes.
  • Integrating classical machine learning with CPs is an active research area.

Purpose of the Study:

  • To develop a human-readable Conformal Predictor (CP) using a genetic algorithm (GA).
  • To evaluate the CP's performance in real-world medical diagnostic datasets.
  • To assess the reliability and utility of confidence measures provided by the CP.

Main Methods:

  • A genetic algorithm (GA) was employed to evolve rule sets for Conformal Prediction (CP).
  • The proposed CP method was applied to two distinct medical diagnostic datasets: breast cancer and ovarian cancer.
  • Performance was benchmarked against classical machine learning techniques.

Main Results:

  • The GA-based CP achieved accuracy comparable to established methods on both breast and ovarian cancer datasets.
  • The CP successfully generated reliable and interpretable confidence measures for each prediction.
  • The human-readable rule sets facilitated understanding of the diagnostic process.

Conclusions:

  • The proposed genetic algorithm-based Conformal Predictor (CP) is a viable and effective tool for medical diagnosis.
  • The method provides accurate predictions alongside trustworthy confidence intervals, aiding clinical decision-making.
  • This approach enhances interpretability in machine learning for critical applications like cancer detection.