Jove
Visualize
Contact Us
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 Concept Videos

Pre-Procedural Guidelines for Assessing Blood Pressure01:10

Pre-Procedural Guidelines for Assessing Blood Pressure

638
Accurate blood pressure assessment is crucial for diagnosing and managing various health conditions. To ensure the reliability of these measurements, healthcare professionals must adhere to standardized pre-procedural guidelines. These guidelines enhance patient safety and improve the overall quality of healthcare. The following steps are essential for obtaining accurate and consistent blood pressure readings, from using the appropriate tools to ensuring effective communication with the...
638
Coronary Artery Disease IV: Preventive Measures01:26

Coronary Artery Disease IV: Preventive Measures

48
Effective preventive measures for coronary artery disease (CAD) focus on controlling modifiable risk factors, including cholesterol abnormalities and lifestyle changes.Cholesterol ManagementFirst, the Mediterranean diet and the American Heart Association advocate for maintaining low-density lipoprotein (LDL) cholesterol levels below 100 mg/dL, with a more stringent recommendation of below 70 mg/dL for individuals at high risk. LDL cholesterol, often termed "bad cholesterol," can lead to the...
48
Factors Influencing Heart Rate01:30

Factors Influencing Heart Rate

5.1K
The heart rate, or pulse rate, is a vital indicator of cardiovascular health. It reflects the number of times the heart beats per minute. Various physiological and environmental factors influence heart rate, increasing or decreasing cardiac output. Understanding these factors is crucial for assessing heart function and identifying potential health issues.
Let us explore the significant factors affecting heart rate, including age, body temperature, posture, acute pain, chemical influences,...
5.1K
Coronary Artery Disease I: Introduction01:30

Coronary Artery Disease I: Introduction

86
Coronary Artery Disease (CAD): An Overview with Scientific InsightsCoronary Artery Disease (CAD), often referred to as C-A-D, is a prevalent blood vessel disorder classified under the broader category of atherosclerosis. Atherosclerosis is a pathological process characterized by the hardening and narrowing of arteries due to the accumulation of atherosclerotic plaques. These plaques are composed of cholesterol, fatty substances, inflammatory cells, calcium, and fibrin, reducing blood flow to...
86
Heart Failure IV: Classification and Diagnostic Evaluation01:30

Heart Failure IV: Classification and Diagnostic Evaluation

55
Heart failure can be classified in various ways, with the most common classifications based on physical activity limitations, disease progression, severity, and treatment strategies.The Functional Classification of Heart Failure divides patients into four categories based on physical activity limitation due to symptom burden.Class I: Patients in this class have cardiac disease but no physical activity limitations. Ordinary activities like walking, climbing stairs, or routine tasks do not cause...
55
Cardiomyopathy III: Hypertrophic Cardiomyopathy01:29

Cardiomyopathy III: Hypertrophic Cardiomyopathy

73
Hypertrophic cardiomyopathy, or HCM, is an autosomal dominant genetic disorder characterized by asymmetric left ventricular hypertrophy without ventricular dilation. It is more common in men and is typically diagnosed in young, athletic adults.EtiologyHCM is primarily genetic and is caused by mutations in genes encoding sarcomeric proteins. Researchers have identified over 1400 mutations across at least 11 different genes. Among these, the most frequently occurring mutations are found in the...
73

You might also read

Related Articles

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

Sort by
Same author

Lightweight Advanced Deep Neural Network (DNN) Model for Early-Stage Lung Cancer Detection.

Diagnostics (Basel, Switzerland)·2024
Same author

An Advanced Lung Carcinoma Prediction and Risk Screening Model Using Transfer Learning.

Diagnostics (Basel, Switzerland)·2024
Same author

Significance of Visible Non-Invasive Risk Attributes for the Initial Prediction of Heart Disease Using Different Machine Learning Techniques.

Computational intelligence and neuroscience·2022
Same author

Machine Translation System Using Deep Learning for English to Urdu.

Computational intelligence and neuroscience·2022

Related Experiment Video

Updated: Sep 26, 2025

Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure
05:16

Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure

Published on: June 10, 2025

243

An Intelligent and Reliable Hyperparameter Optimization Machine Learning Model for Early Heart Disease Assessment

Syed Immamul Ansarullah1, Syed Mohsin Saif2, Syed Abdul Basit Andrabi3

  • 1Lecturer at the Department of Computer Science, Govt. Degree College Sumbal, J&K, India.

Journal of Healthcare Engineering
|April 22, 2022
PubMed
Summary

A new machine learning model accurately predicts heart disease risk using low-cost, noninvasive attributes. This optimized random forest model offers high accuracy, especially for rural healthcare access.

More Related Videos

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.0K

Related Experiment Videos

Last Updated: Sep 26, 2025

Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure
05:16

Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure

Published on: June 10, 2025

243
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.0K

Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Public Health

Background:

  • Heart disease poses a significant global health burden, causing suffering and disability.
  • Existing risk assessment models may have limitations, particularly in resource-limited settings.

Purpose of the Study:

  • To develop and validate a low-cost, noninvasive heart disease risk evaluation model using machine learning.
  • To compare the performance of various machine learning algorithms for heart disease risk prediction.

Main Methods:

  • Utilized hyperparameter optimization of machine learning techniques for model development.
  • Employed recursive feature elimination for selecting and ranking risk attributes.
  • Validated attribute importance with medical domain experts.
  • Tested and compared decision tree, k-nearest neighbor, random forest, and support vector machine models.

Main Results:

  • The optimized random forest model demonstrated superior performance across all evaluated metrics (sensitivity, specificity, precision, accuracy, AUROC).
  • The model achieved the lowest misclassification rate compared to other tested algorithms.
  • Simulations indicated the model outperforms existing risk assessment approaches in predictive accuracy.

Conclusions:

  • The developed machine learning model provides an accurate and cost-effective tool for heart disease risk prediction.
  • This model is particularly valuable for rural areas with limited access to primary healthcare services.
  • Further research is recommended to explore newly identified aspects of heart disease.