Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Dangling centrality highlights critical nodes by evaluating network stability through link removal.

Scientific reports·2025
Same author

Enhancing explainability in epidemiological predictions using fuzzy logic integrated with machine and deep learning algorithms.

Scientific reports·2025
Same author

A novel fuzzy three-valued logic computational framework in machine learning for medicine dataset.

Computers in biology and medicine·2025
Same author

Fuzzy machine learning logic utilization on hormonal imbalance dataset.

Computers in biology and medicine·2024

Related Experiment Video

Updated: Jan 11, 2026

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.7K

Robust missing data reconstruction in schizophrenia using tracking-removed autoencoder with fuzzy confidence

Moazzama Mateen1, Ubaida Fatima2

  • 1Department of Mathematics, NED University of Engineering and Technology, Karachi, Sindh, Pakistan. moazzamamateen190@gmail.com.

Scientific Reports
|November 19, 2025
PubMed
Summary

This study introduces a novel deep learning framework, TRAE+MVPT, to accurately predict missing clinical data in schizophrenia research. The model enhances diagnostic reliability and interpretability by treating missing information as learnable and providing fuzzy confidence measures for imputed data.

Keywords:
Fuzzy confidenceMedical diagnosisMulti-view progressive trainingSchizophrenia datasetTracking removed autoencoder

More Related Videos

Measurement of Fronto-limbic Activity Using an Emotional Oddball Task in Children with Familial High Risk for Schizophrenia
13:08

Measurement of Fronto-limbic Activity Using an Emotional Oddball Task in Children with Familial High Risk for Schizophrenia

Published on: December 2, 2015

9.3K
Generalized Psychophysiological Interaction PPI Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease
09:38

Generalized Psychophysiological Interaction PPI Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease

Published on: November 14, 2017

15.5K

Related Experiment Videos

Last Updated: Jan 11, 2026

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.7K
Measurement of Fronto-limbic Activity Using an Emotional Oddball Task in Children with Familial High Risk for Schizophrenia
13:08

Measurement of Fronto-limbic Activity Using an Emotional Oddball Task in Children with Familial High Risk for Schizophrenia

Published on: December 2, 2015

9.3K
Generalized Psychophysiological Interaction PPI Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease
09:38

Generalized Psychophysiological Interaction PPI Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease

Published on: November 14, 2017

15.5K

Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Psychiatry

Background:

  • Schizophrenia diagnosis is challenging due to symptom heterogeneity and reliance on subjective evaluations.
  • Incomplete clinical data is a major barrier in developing accurate predictive models for schizophrenia.
  • Existing methods often treat missing data as absent, limiting model robustness.

Purpose of the Study:

  • To develop an advanced deep learning framework for predicting missing features in schizophrenia datasets.
  • To improve the interpretability and reliability of reconstructed clinical features for better diagnostic clarity.
  • To integrate fuzzy confidence measures for quantifying the reliability of imputed data.

Main Methods:

  • Developed a deep learning framework integrating Tracking-Removed Autoencoder (TRAE) with Multi-View Progressive Training (MVPT).
  • Treated missing data as learnable information, incorporating gaps directly into the training process.
  • Applied fuzzy confidence measures to assess the reliability of imputed features, generating linguistic descriptors.

Main Results:

  • The TRAE+MVPT framework effectively predicts missing features in incomplete schizophrenia datasets.
  • The model demonstrates robustness by learning from missing data patterns.
  • Fuzzy confidence measures provide interpretable assessments of imputed data reliability.

Conclusions:

  • The novel TRAE+MVPT framework enhances psychiatric data modeling by improving interpretability and reliability of reconstructed features.
  • This approach addresses diagnostic uncertainty in schizophrenia, supporting transparent decision-making and clearer symptom tracking.
  • The integration of MVPT with fuzzy confidence measures offers a rigorous method for evaluating imputation quality in schizophrenia research.