Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Genetic Screens

Genetic Screens

Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which...

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...

Quantifying and Rejecting Outliers: The Grubbs Test

Quantifying and Rejecting Outliers: The Grubbs Test

Sometimes, a data set can have a recorded numerical observation that greatly deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier. To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...

You might also read

Related Articles

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

Sort by

Same journal

Readability of AI-Generated Patient Information on Glucagon-Like Peptide-1 Receptor Agonists.

JMIR bioinformatics and biotechnology·2026

Same journal

Random Survival Forest Versus Elastic-Net Regularized Cox Regression for Survival Prediction in Acute Myeloid Leukemia at Distinct Treatment Time Points: Model Performance Comparison Study.

JMIR bioinformatics and biotechnology·2026

Same journal

The AudioGene Translational Dashboard for Diagnosing Autosomal Dominant Nonsyndromic Hearing Loss: Phenotypic Data Visualization and Analysis Study.

JMIR bioinformatics and biotechnology·2026

Same journal

A Strategic Partnership to Advance AI Applications in Genomics and Bioinformatics for Health Innovation.

JMIR bioinformatics and biotechnology·2026

Same journal

Prevalence and Associated Risk Factors of Bovine Fasciolosis in Bahir Dar, Ethiopia: Cross-Sectional Study.

JMIR bioinformatics and biotechnology·2026

Same journal

Unpacking Genomic Biomarkers for Programmed Cell Death Receptor-1 Immunotherapy Success in Non-Small Cell Lung Cancer Using Deep Neural Networks: Quantitative Study.

JMIR bioinformatics and biotechnology·2026

See all related articles

Search research articles

Home
Temporal Reproducibility Of A Genetic Algorithm-derived Health Risk Score: Standardized Out-of-fold Validation Framework (2021-2023).

Home
Temporal Reproducibility Of A Genetic Algorithm-derived Health Risk Score: Standardized Out-of-fold Validation Framework (2021-2023).

Related Experiment Video

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Temporal Reproducibility of a Genetic Algorithm-Derived Health Risk Score: Standardized Out-of-Fold Validation

Yoichiro Aoki¹, Hiroki Takeda², Kinichi Yokota³

¹Yoshida Hospital-Keiyukai Medical Corporation, 1-2, Nishi 4-chome, 4-jyo, Asahikawa, Hokkaido, 070-0054, Japan, 81 166-23-0685.

JMIR Bioinformatics and Biotechnology

|April 21, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

This study demonstrates that a genetic algorithm (GA)-derived health risk score is temporally reproducible across three annual cohorts. Standardized evaluation ensures reliable health risk stratification for preventive screening.

Keywords:

AUC ROC area under the receiver operating characteristic curve cross-validation genetic algorithm health risk scoring preventive medicine reproducibility

More Related Videos

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Related Experiment Videos

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Area of Science:

Data-driven health risk stratification
Preventive health screening methodologies
Computational health analytics

Background:

Genetic algorithm (GA)-based scoring offers a data-driven approach for health risk stratification.
Performance estimates require strict separation of preprocessing, optimization, and evaluation within a validation framework.
Temporal reproducibility and uncertainty quantification are crucial for translational reliability in health screening.

Purpose of the Study:

Evaluate the temporal reproducibility of a GA-derived composite health risk score across three annual cohorts (2021-2023).
Assess the score's robustness to missing HbA1c data using a sensitivity analysis.
Validate the score under a standardized out-of-fold (OOF) evaluation pipeline.

Main Methods:

Analyzed annual health examination datasets (2021-2023) with an identical preprocessing and modeling pipeline.
Included 13 clinical indicators and 8 lifestyle variables; outcome based on A-D grading framework.
Employed stratified K-fold cross-validation for GA optimization and logistic regression for OOF probability prediction; quantified performance using AUC and Brier score.

Main Results:

Out-of-fold AUC values remained stable across cohorts (0.810-0.814) with overlapping confidence intervals.
Brier scores indicated consistent overall predictive performance (0.172-0.176).
Excluding HbA1c minimally impacted discrimination, confirming robustness.

Conclusions:

The GA-derived composite risk score demonstrates stable temporal discrimination and consistent predictive performance.
Highlights the importance of standardized, prespecified evaluation frameworks for risk stratification models.
Transparent uncertainty quantification is essential for validating models using routine health screening data.