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

Acute Coronary Syndrome III: Diagnostic Studies

Acute Coronary Syndrome III: Diagnostic Studies

Diagnosing acute coronary syndrome or ACS begins with a thorough patient history. Notable symptoms include central, crushing chest pain radiating to the left arm, neck, jaw, or back, along with shortness of breath, sweating (diaphoresis), nausea, vomiting, dizziness, and palpitations.It is crucial to note any history of cardiac illnesses and assess risk factors, including age, gender, smoking, hypertension, diabetes, hyperlipidemia, and a sedentary lifestyle.During physical examination, vital...

Angina III: Clinical Manifestations and Assessment

Angina III: Clinical Manifestations and Assessment

Angina manifests as chest pain, tightness, or squeezing discomfort typically located behind the breastbone. It can radiate to the neck, jaw, shoulders, and inner aspects of the upper arms, most commonly the left arm. Patients may experience shortness of breath, fatigue, profuse sweating, dizziness, indigestion, heartburn, palpitations, anxiety, and vomiting as accompanying symptoms. This pain often lasts a few minutes and is triggered by physical exertion, emotional stress, heavy meals, or cold...

You might also read

Related Articles

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

Sort by

Same author

Geotechnical challenges of urban expansion in Mila Town (NE Algeria): an integrated Engineering Ground Model (EGM) approach.

Scientific reports·2026

Same author

Metaheuristic-optimized interaction-aware deep learning with large language model assistance for data-driven water quality prediction.

Scientific reports·2026

Same author

Human-inspired hyperparameter optimization for long-horizon forecasting of freshwater and desalination per-capita dynamics.

Scientific reports·2026

Same author

An optimization-driven hierarchical deep learning approach using the Gray Langurs algorithm for data-driven seismic activity prediction.

Scientific reports·2026

Same author

Accurate surgery time prediction (ASTP) strategy based on artificial intelligence techniques.

Scientific reports·2026

Same author

Deposit characterizations and engineering-geotechical modeling for sustainable urbanisation in the Mila basin (NE Algeria).

Scientific reports·2026

Same journal

MT-MRI for detection of renal interstitial fibrosis in renovascular disease.

Scientific reports·2026

Same journal

Detection of underground objects from GPR data using a lightweight YOLO-based approach.

Scientific reports·2026

Same journal

Early systemic inflammatory-metabolic trajectory phenotypes are associated with survival outcomes in metastatic renal cell carcinoma treated with nivolumab.

Scientific reports·2026

Same journal

Water balance components in a dry-seeded rice-wheat system: Untangling the effects of tillage and mulching practices.

Scientific reports·2026

Same journal

Topological approaches to quantum tensor train compression via ZX-calculus and SVD.

Scientific reports·2026

Same journal

determinants of flood impacts and adaptive capacity among market vendors in Walukuba-Masese, Jinja city, Uganda.

Scientific reports·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 17, 2026

Semi-automated Optical Heartbeat Analysis of Small Hearts

Semi-automated Optical Heartbeat Analysis of Small Hearts

Published on: September 16, 2009

A new ensemble heart attack diagnosis (EHAD) model using artificial intelligence techniques.

Bahaa El-Din Waleed¹, El-Sayed M El-Kenawy^2,3, Sherif Ibrahim⁴

¹Department of Applied Health Sciences, Higher Technological Institute of Applied Health Sciences, Mansoura, Egypt. bahaaafia@std.mans.edu.e.g.

Scientific Reports

|September 15, 2025

Summary

This summary is machine-generated.

A new ensemble model for diagnosing heart attacks (myocardial infarctions) shows improved accuracy over existing methods. This hybrid approach combines multiple machine learning classifiers for faster and more precise heart attack detection.

Keywords:

Artificial intelligence Diagnosis system Ensemble model Feature selection Heart attack

More Related Videos

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

In Silico Clinical Trials for Cardiovascular Disease

In Silico Clinical Trials for Cardiovascular Disease

Published on: May 27, 2022

Related Experiment Videos

Last Updated: Jan 17, 2026

Semi-automated Optical Heartbeat Analysis of Small Hearts

Semi-automated Optical Heartbeat Analysis of Small Hearts

Published on: September 16, 2009

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

In Silico Clinical Trials for Cardiovascular Disease

In Silico Clinical Trials for Cardiovascular Disease

Published on: May 27, 2022

Area of Science:

Cardiology
Artificial Intelligence
Machine Learning

Background:

Myocardial infarctions (heart attacks) are a major global cause of death.
Current machine learning and artificial intelligence diagnostic methods lack optimal accuracy.
Improved diagnostic accuracy is crucial for better patient outcomes.

Purpose of the Study:

To introduce a novel hybrid diagnostic model for heart attack detection.
To enhance the accuracy and speed of heart attack diagnosis using ensemble techniques.

Main Methods:

Developed the Ensemble Heart Attack Diagnosis (EHAD) model.
Utilized Ensemble Classification Technique (ECT) integrating Support Vector Machine (SVM), Long Short-Term Memory (LSTM), and Artificial Neural Network (ANN).
Employed Majority Voting (MV) for final decision-making.

Main Results:

The EHAD model demonstrated superior performance compared to existing models.
EHAD achieved high scores in recall, precision, F1 score, and accuracy.
Statistical analysis confirmed the model's effectiveness.

Conclusions:

The proposed EHAD model offers a more accurate and efficient approach to heart attack diagnosis.
This hybrid ensemble method represents a significant advancement in AI-driven cardiovascular diagnostics.
Further development can improve patient outcomes through precise and timely diagnosis.