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

Pharmacokinetic Models: Comparison and Selection Criterion

Pharmacokinetic Models: Comparison and Selection Criterion

Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.

You might also read

Related Articles

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

Sort by

Same author

Competitive roles of plasticity, elasticity, and radiation in heat/mass transfer of Casson-Williamson-Maxwell nanofluid over a porous stretching sheet.

Discover nano·2026

Same author

Effect of Aluminum Oxide Air Abrasion on Shear Bond Strength of Composite Resin to Human Dentin Using Two Adhesive Systems.

Journal of pharmacy & bioallied sciences·2026

Same author

Class-balanced dermoscopic lesion segmentation using MoG-LISA and optimized Swin-UNet via the GM-FDE framework.

iScience·2026

Same author

Comparative Evaluation of Apically Extruded Debris Using Two Rotary and Two Reciprocating File Systems: An <i>In Vitro</i> Study.

Journal of pharmacy & bioallied sciences·2025

Same author

Soret Effect and Chemical Process on MHD Oscillatory Flow in a Physiological Fluid.

Applied bionics and biomechanics·2025

Same author

Enhancing e-waste management: a novel light gradient AdaBoost support vector classification approach.

Environmental monitoring and assessment·2025

Search research articles

Related Experiment Video

Updated: May 4, 2026

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

Published on: January 8, 2013

Dynamic feature selection and quantum representation for precise heart disease prediction: Quantum-HeartDiseaseNet

Liza M Kunjachen¹, R Kavitha²

¹Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, Tamil Nadu, India.

Computer Methods in Biomechanics and Biomedical Engineering

|February 5, 2025

Summary

This study introduces Quantum-HeartDiseaseNet for early cardiovascular disease prediction. The novel framework achieves high accuracy, improving patient outcomes through advanced feature selection and a quantum-based deep learning model.

Keywords:

Gated Recurrent Unit Heart disease attention mechanism deep learning feature selection risk prediction

More Related Videos

In Silico Clinical Trials for Cardiovascular Disease

In Silico Clinical Trials for Cardiovascular Disease

Published on: May 27, 2022

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Related Experiment Videos

Last Updated: May 4, 2026

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

Published on: January 8, 2013

In Silico Clinical Trials for Cardiovascular Disease

In Silico Clinical Trials for Cardiovascular Disease

Published on: May 27, 2022

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Area of Science:

Cardiology and Artificial Intelligence
Biomedical Data Science
Quantum Computing Applications

Background:

Cardiovascular disease (CVD) is a primary cause of global mortality.
Accurate and early prediction of CVD is crucial for effective patient management.
Existing prediction models face challenges with complex data and imbalanced datasets.

Purpose of the Study:

To develop a novel, high-performance framework for heart disease risk prediction.
To enhance diagnostic accuracy and efficiency using advanced computational techniques.
To address data imbalance issues in cardiovascular datasets.

Main Methods:

Integration of a Dynamic Opposite Pufferfish Optimization Algorithm for optimal feature selection.
Utilization of a Quantum Attention-based Bidirectional Gated Recurrent Unit (QABiGRU) for heart disease classification.
Application of Synthetic Minority Oversampling Technique (SMOTE) to mitigate data imbalance.

Main Results:

The Quantum-HeartDiseaseNet model demonstrated superior performance across three heart disease datasets.
Achieved high diagnostic metrics: 98.87% accuracy, 98.74% precision, and 98.56% recall.
Outperformed conventional heart disease prediction methods in experimental evaluations.

Conclusions:

The proposed Quantum-HeartDiseaseNet framework is effective for early and accurate heart disease prediction.
The combination of advanced optimization and quantum deep learning significantly improves diagnostic capabilities.
This approach holds promise for enhancing patient outcomes in cardiovascular care.