Jove
Visualize
Contact Us

Related Concept Videos

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine08:27

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

1.6K
The protocol described in this paper utilizes the directional gradient histogram technique to extract the characteristics of concrete image samples under various vibration states. It employs a support vector machine for machine learning, resulting in an image recognition method with minimal training sample requirements and low computer performance...
1.6K
Censoring Survival Data01:09

Censoring Survival Data

529
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
529
Constructing and Visualizing Models using Mime-based Machine-learning Framework06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

2.3K
Mime is a flexible computational framework to construct a machine learning-based integration model with elegant performance. Here, we provide a detailed step-by-step procedure for developing predictive models with high accuracy, leveraging complex datasets to identify critical genes associated with disease progression, patient outcomes, and therapeutic response.
2.3K
Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine07:05

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

9.6K
This article describes a novel method to estimate proprioceptive drift on a 2D plane using the mirror illusion and combining a psychophysical procedure with an analysis using machine...
9.6K
Asthma Detection Research Based on Voice Signal Processing and Machine Learning04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

939
This study employed voice signal analysis and machine learning methods, utilizing MATLAB to extract distinctive voice features for non-invasive early detection of asthma. The Support Vector Machine (SVM) and Random Forest (RF) algorithms demonstrated comparable performance in terms of overall classification accuracy, although SVM may achieve a better balance between sensitivity and...
939
IR-TEx: An Open Source Data Integration Tool for Big Data Transcriptomics Designed for the Malaria Vector Anopheles gambiae08:22

IR-TEx: An Open Source Data Integration Tool for Big Data Transcriptomics Designed for the Malaria Vector Anopheles gambiae

6.6K
IR-TEx explores insecticide resistance-related transcriptional profiles in the species Anopheles gambiae. Provided here are full instructions for using the application, modifications for exploring multiple transcriptomic datasets, and using the framework to build an interactive database for collections of transcriptomic data from any organism, generated in any...
6.6K

You might also read

Related Articles

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

Sort by
Same author

A New Estimation Algorithm for Destructive Cure Model: Illustration with Exponentially Weighted Poisson Competing Risks.

Communications in statistics: Simulation and computation·2026
Same author

A PINN-driven game-theoretic framework in limited data photoacoustic tomography.

Inverse problems·2025
Same author

Machine Learning Approach for Analyzing Mixed Case Interval Censored Data with a Cured Subgroup.

Advances in statistical analysis : AStA : a journal of the German Statistical Society·2025
Same author

Likelihood-Based Inference for Semi-Parametric Transformation Cure Models with Interval Censored Data.

Communications in statistics: Simulation and computation·2025
Same author

A Neural Network Integrated Accelerated Failure Time-Based Mixture Cure Model.

Statistics and computing·2025
Same author

A New Cure Rate Model with Discrete and Multiple Exposures.

Communications in statistics: Simulation and computation·2025
Same journal

Neural posterior estimation on exponential random graph models: evaluating bias and implementation challenges.

Statistics and computing·2026
Same journal

Subgroup Analysis of Differential Networks with Latent Variables.

Statistics and computing·2026
Same journal

Non-negative matrix factorization algorithms generally improve topic model fits.

Statistics and computing·2026
Same journal

Approximating evidence via bounded harmonic means.

Statistics and computing·2026
Same journal

Efficient Inference in First Passage Time Models.

Statistics and computing·2026
Same journal

Optimal <i>F</i>-score Matching for Bipartite Record Linkage.

Statistics and computing·2026
See all related articles
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 Experiment Video

Updated: Jan 20, 2026

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.6K

A Support vector machine-based mixture cure model for mixed case interval censored data.

Suvra Pal1,2, Wisdom Aselisewine1

  • 1Department of Mathematics, University of Texas at Arlington, Arlington, Texas 76019 USA.

Statistics and Computing
|January 19, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel semi-parametric model for interval censored data with a cured subgroup, utilizing support vector machines (SVM) for improved cure probability estimation and Cox models for survival analysis.

Keywords:
Cure RateEM AlgorithmMachine learningPlatt ScalingPredictive Accuracy

More Related Videos

Censoring Survival Data
01:09

Censoring Survival Data

529
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.3K

Related Experiment Videos

Last Updated: Jan 20, 2026

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.6K
Censoring Survival Data
01:09

Censoring Survival Data

529
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.3K

Area of Science:

  • Biostatistics
  • Machine Learning
  • Survival Analysis

Background:

  • Mixed case interval censored (MCIC) data presents unique challenges in statistical analysis.
  • Identifying a 'cured' subgroup, where individuals are never susceptible to the event, is crucial for accurate modeling.

Purpose of the Study:

  • To develop a novel semi-parametric two-component model for analyzing MCIC data with a cured subgroup.
  • To integrate a support vector machine (SVM) for enhanced cure probability modeling and a Cox proportional hazards structure for survival distribution analysis.
  • To address the limitations of traditional generalized linear models in capturing complex covariate effects within MCIC data.

Main Methods:

  • A semi-parametric two-component model combining SVM for cure probability and Cox proportional hazards for uncured survival.
  • Development of an expectation maximization algorithm for parameter estimation.
  • Simulation studies to evaluate model performance and superiority.

Main Results:

  • The proposed SVM-based model demonstrates superior performance compared to traditional methods in simulation studies.
  • Successful application of the model to NASA's Hypobaric Decompression Sickness Data.
  • The model effectively captures complex covariate effects and handles interval censored data with a cured subgroup.

Conclusions:

  • The novel SVM-based semi-parametric model offers a powerful and flexible approach for analyzing MCIC data with cured subgroups.
  • This work represents the first application of machine learning algorithms to MCIC data analysis in the presence of a cured population.
  • The model provides improved accuracy and interpretability for survival data analysis in various scientific fields.