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

Pulse rhythm

Pulse rhythm

Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...

Pathophysiology of Heart Failure

Pathophysiology of Heart Failure

Heart failure (HF) is a progressive syndrome involving ventricles that leads to inadequate cardiac output. It can be classified based on location and output or ejection fraction. Ejection fraction (EF) is an essential measurement in the diagnosis and surveillance of HF. Reduced EF corresponds to systolic heart failure (HFrEF). However, HF with preserved ejection fraction (HFpEF) is becoming increasingly prevalent. Also known as diastolic HF, this form of HF is related to aging. The...

You might also read

Related Articles

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

Sort by

Same author

HMLA: A hybrid machine learning approach for enhancing stroke prediction models with missing data imputation techniques.

Scientific reports·2025

Same author

Segmentation and classification on chest radiography: a systematic survey.

The Visual computer·2022

Same author

Effect of vascular volume and edema on wave propagation in canine lungs.

Journal of applied physiology (Bethesda, Md. : 1985)·1990

Same author

Propagation of stress waves in inflated sheep lungs.

Journal of applied physiology (Bethesda, Md. : 1985)·1989

Same author

Creatine kinase MM isoforms in serum after cardiac surgery.

Clinical chemistry·1988

Same author

Myocardial tissue characterization using pattern recognition procedures on backscattered ultrasonic signals.

Ultrasonic imaging·1986

Same journal

Correction: Luca et al. Global and Regional Diagnostic Results of Progress Toward Cervical Cancer Elimination, According to the WHO Strategy: A Systematic Literature Review with Narrative Synthesis. <i>Diagnostics</i> 2026, <i>16</i>, 1224.

Diagnostics (Basel, Switzerland)·2026

Same journal

Association Between Systemic Inflammatory Response Biomarkers and Disease Activity in Systemic Lupus Erythematosus: A Multi-Center Retrospective Study.

Diagnostics (Basel, Switzerland)·2026

Same journal

Vertebrogenic Low Back Pain and Basivertebral Nerve Ablation: A Review of Mechanisms, Imaging-Driven Selection, and Clinical Outcomes.

Diagnostics (Basel, Switzerland)·2026

Same journal

Multivalvular Carcinoid Heart Disease: The Role of Echocardiography in Diagnosis and Selection for Heterotopic Bicaval Valve Implantation.

Diagnostics (Basel, Switzerland)·2026

Same journal

Data-Efficient and Explainable Multimodal Survival Prediction in NSCLC Using Deep Image Embeddings, Clinical Variables, and Gradient-Boosted Trees.

Diagnostics (Basel, Switzerland)·2026

Same journal

Anomalous Left Coronary Artery from the Pulmonary Artery: Cinematic Volume Rendering Technique for Enhanced Anatomic Visualization.

Diagnostics (Basel, Switzerland)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 28, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

An Integrated Machine Learning Approach for Congestive Heart Failure Prediction.

M Sheetal Singh¹, Khelchandra Thongam¹, Prakash Choudhary²

¹Department of Computer Science and Engineering, National Institute of Technology Manipur, Langol, Imphal 795004, Manipur, India.

Diagnostics (Basel, Switzerland)

|April 13, 2024

Summary

This summary is machine-generated.

Early detection of congestive heart failure (CHF) is crucial. This study uses machine learning, specifically a deep neural network, to accurately predict CHF, potentially reducing healthcare costs and improving patient outcomes.

Keywords:

C4.5 CHF prediction CHS DNN KNN imputation

More Related Videos

In Silico Clinical Trials for Cardiovascular Disease

In Silico Clinical Trials for Cardiovascular Disease

Published on: May 27, 2022

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction

Published on: February 13, 2021

Related Experiment Videos

Last Updated: Jun 28, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

In Silico Clinical Trials for Cardiovascular Disease

In Silico Clinical Trials for Cardiovascular Disease

Published on: May 27, 2022

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction

Published on: February 13, 2021

Area of Science:

Cardiology
Artificial Intelligence
Health Informatics

Background:

Congestive heart failure (CHF) is a significant global health concern, affecting over 26 million people worldwide.
The prevalence of CHF is increasing, highlighting the need for effective early detection and diagnosis methods.
Current diagnostic costs can be reduced through advanced prediction techniques.

Purpose of the Study:

To enhance the early diagnosis of congestive heart failure (CHF) using machine learning.
To reduce the cost of CHF diagnosis by utilizing a minimum set of features for prediction.
To compare the performance of a deep neural network (DNN) with other machine learning classifiers for CHF prediction.

Main Methods:

Utilized the Cardiovascular Health Study (CHS) dataset for training and evaluation.
Implemented a novel pre-processing technique integrating C4.5 for feature selection/outlier removal and K-nearest neighbor (KNN) for missing data imputation.
Compared a deep neural network (DNN) classifier against six traditional machine learning algorithms (KNN, LR, NB, RF, SVM, DT) using seven statistical metrics.

Main Results:

The proposed integrated approach, particularly the DNN, demonstrated superior performance in CHF prediction compared to other ML algorithms.
Achieved high performance metrics: 97.03% F1-score, 95.30% accuracy, 96.49% sensitivity, and 97.58% precision.
The study's methodology effectively reduced the number of medical tests required, indicating potential cost savings for patients.

Conclusions:

The developed machine learning model, especially the DNN, offers a promising tool for accurate and cost-effective early prediction of congestive heart failure.
The integrated pre-processing technique enhances data quality and model performance for cardiovascular health studies.
Early prediction of CHF through advanced AI can significantly reduce mortality and morbidity associated with the condition.