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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Estimating classification probabilities in high-dimensional diagnostic studies.

Inka J Appel1, Wolfram Gronwald, Rainer Spang

  • 1Institute of Functional Genomics, University of Regensburg, 93053 Regensburg, Germany. inka.appel@klinik.uni-regensburg.de

Bioinformatics (Oxford, England)
|July 26, 2011
PubMed
Summary
This summary is machine-generated.

Accurate classification of biological data requires assessing individual case probabilities, not just average accuracy. A novel method combining local cross-validation and monotone regression offers superior probability estimation for metabolomic profiling.

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Last Updated: May 30, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Published on: January 11, 2020

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Biostatistics

Background:

  • Traditional classification algorithms for high-dimensional biological data focus on average accuracy.
  • Assessing individual case reliability in clinical diagnosis requires estimating class probabilities.
  • Probability estimation is less developed in biological data analysis compared to classification algorithms.

Purpose of the Study:

  • To compare various probability estimators for classifying metabolomic profiles.
  • To identify reliable methods for estimating class probabilities in biological data.
  • To address the limitations of existing probability estimation techniques in practice.

Main Methods:

  • Comparison of several probability estimators for metabolomic data classification.
  • Evaluation based on sparseness biases, estimator calibration, and variance.
  • Assessment of performance in identifying highly reliable classifications.

Main Results:

  • Several tested probability estimators exhibited artifacts compromising their practical use.
  • A superior method combining local cross-validation error rates and monotone regression was identified.
  • This combined approach proved effective for metabolomic profiling.

Conclusions:

  • Standard classification accuracy metrics are insufficient for individual case reliability in clinical diagnosis.
  • Probability estimation is crucial for assessing the certainty of individual classifications in biological data.
  • The proposed method offers a more reliable approach to probability estimation in metabolomic profiling.