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Ischemic Heart Disease: Overview

Ischemic Heart Disease: Overview

Ischemic heart disease occurs when the heart's blood supply dwindles, causing an ominous lack of oxygen and nutrients. This deficiency, stemming from reduced or obstructed blood flow, spells danger, leading to heart muscle damage and dysfunction.
Atherosclerosis, the primary malefactor, orchestrates this dangerous condition. It manifests as the accumulation of fatty deposits, akin to insidious plaques, within arterial walls. As time elapses, these plaques metamorphose, hardening and...

Heart Failure I: Introduction

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Heart failure refers to a clinical syndrome caused by structural or functional cardiac disorders that prevent the heart from pumping an adequate amount of blood to meet the body's metabolic needs. This condition often arises from myocardial infarction or ischemia, leading to decreased cardiac output, reduced tissue perfusion, impaired gas exchange, fluid volume imbalance, and decreased functional ability.Heart failure can result from disruptions in the mechanisms that regulate cardiac output...

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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...

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

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Machine-learning Algorithms for Ischemic Heart Disease Prediction: A Systematic Review.

Salam H Bani Hani^1,2, Muayyad M Ahmad¹

¹Faculty of Nursing, The University of Jordan, Amman, Jordan.

Current Cardiology Reviews

|June 13, 2022

Summary

Machine learning algorithms can accurately predict ischemic heart disease, aiding clinicians in patient data interpretation and treatment decisions. This review evaluates the most effective algorithms for improved cardiovascular care.

Keywords:

Big data PRISMA data mining ischemic heart disease machine-learning algorithms prediction

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Area of Science:

Cardiology
Medical Informatics
Artificial Intelligence

Background:

Ischemic heart disease (IHD) poses a significant global health burden.
Accurate prediction of IHD is crucial for timely intervention and improved patient outcomes.
Machine learning (ML) offers promising tools for enhancing IHD risk assessment.

Approach:

This systematic review adhered to PRISMA guidelines.
A comprehensive literature search was conducted across major scientific databases (PubMed/MEDLINE, Science Direct, CINAHL, IEEE Explore).
Included studies were published between 2017 and 2021, focusing on ML algorithms for IHD prediction.

Key Points:

Thirteen articles were eligible for inclusion, analyzing ML algorithms for IHD prediction.
Commonly used ML algorithms include both supervised and unsupervised learning techniques.
Algorithm accuracy and impact on clinical outcomes were key evaluation themes.

Conclusions:

ML algorithms demonstrate potential in assisting clinicians with patient data interpretation for IHD.
Optimal ML algorithm selection can improve the quality of care and support evidence-based clinical decision-making.
ML facilitates personalized patient management, particularly for those requiring invasive procedures like catheterization.