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Related Concept Videos

Classification of Illness01:17

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Family-wise automatic classification in schizophrenia.

René C W Mandl1, Rachel M Brouwer, Wiepke Cahn

  • 1Department of Psychiatry, University Medical Center Utrecht, Rudolf Magnus Institute of Neuroscience, The Netherlands. r.mandl@umcutrecht.nl

Schizophrenia Research
|July 24, 2013
PubMed
Summary
This summary is machine-generated.

Early schizophrenia risk classification improves with family history data. Including heritable features in support vector machine models boosted accuracy from 54% to 72% for early intervention.

Keywords:
Automatic classificationContextual informationEarly detectionHeritabilityMRISupport vector machine

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Published on: December 2, 2015

Area of Science:

  • Psychiatric genetics
  • Machine learning in healthcare
  • Biomedical data analysis

Background:

  • Schizophrenia risk classification is crucial for early intervention.
  • Current methods often overlook familial (heritable) disease markers.
  • Integrating genetic and familial data can enhance predictive accuracy.

Purpose of the Study:

  • To evaluate the impact of including familial heritable features on schizophrenia risk classification accuracy.
  • To determine if incorporating contextual familial information improves predictive models.

Main Methods:

  • Utilized a support vector machine (SVM) model for classification.
  • Compared classification accuracy using individual data versus data including relatives' heritable features.
  • Assessed performance metrics to quantify improvements.

Main Results:

  • Classification accuracy increased from 0.54 to 0.72 when familial features were included.
  • The support vector machine model demonstrated significant improvement with contextual data.
  • Heritable components in classification features strongly benefit from this approach.

Conclusions:

  • Incorporating heritable features from relatives significantly enhances schizophrenia risk classification accuracy.
  • This method offers a more robust screening tool for early intervention in schizophrenia.
  • The approach is particularly valuable for diseases with a significant genetic component.