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Related Concept Videos

Heart Failure IV: Classification and Diagnostic Evaluation01:30

Heart Failure IV: Classification and Diagnostic Evaluation

73
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...
73
Cardiomyopathy V: Interprofessional Care01:29

Cardiomyopathy V: Interprofessional Care

61
Managing cardiomyopathy involves addressing underlying or precipitating causes, treating heart failure with medications, and implementing dietary changes and a balanced exercise and rest regimen.Lifestyle ModificationsCardiomyopathy patients should adopt a low-sodium diet to reduce fluid retention and manage heart failure. A personalized exercise and rest plan helps maintain physical fitness without overstraining the heart. Avoiding alcohol and tobacco is essential to prevent further damage to...
61
Survival Tree01:19

Survival Tree

178
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
178
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

298
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
298
Heart Failure I: Introduction01:27

Heart Failure I: Introduction

124
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...
124
Cardiomyopathy III: Hypertrophic Cardiomyopathy01:29

Cardiomyopathy III: Hypertrophic Cardiomyopathy

86
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...
86

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Survival prediction among heart patients using machine learning techniques.

Abdulwahab Ali Almazroi1

  • 1University of Jeddah, College of Computing and Information Technology at Khulais, Department of Information Technology, Jeddah, Saudi Arabia.

Mathematical Biosciences and Engineering : MBE
|December 14, 2021
PubMed
Summary

This study evaluated machine learning algorithms for cardiovascular disease prediction. Decision trees outperformed logistic regression, support vector machines, and artificial neural networks, showing superior accuracy in detecting heart conditions.

Keywords:
artificial neural networkscardiovascular diseasesdecision treesmachine learningsurvival prediction

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Area of Science:

  • Medical Informatics
  • Computational Biology
  • Machine Learning in Healthcare

Background:

  • Cardiovascular diseases (CVDs) are the leading cause of global mortality, accounting for 17.9 million deaths annually.
  • The high mortality rate from CVDs necessitates advanced methods for early detection and diagnosis.
  • Machine learning (ML) techniques are increasingly explored for predicting and managing cardiovascular health.

Purpose of the Study:

  • To independently verify the performance of standard benchmark ML algorithms for cardiovascular disease prediction.
  • To identify the most effective ML algorithm for early detection and diagnosis of heart-related diseases.
  • To compare the accuracy of Decision Trees, Logistic Regression, Support Vector Machines, and Artificial Neural Networks on a curated dataset.

Main Methods:

  • Utilized a standard, well-curated dataset for cardiovascular disease prediction.
  • Implemented and evaluated benchmark machine learning algorithms: Decision Trees, Logistic Regression, Support Vector Machines (SVM), and Artificial Neural Networks (ANN).
  • Assessed algorithm performance using a range of standard evaluation metrics.

Main Results:

  • Decision Trees demonstrated superior performance compared to Logistic Regression, SVM, and ANN.
  • Decision Trees achieved 14% higher accuracy than the average performance of the other evaluated algorithms.
  • Contrary to some studies, Artificial Neural Networks were found to be less competitive than Decision Trees and SVMs in this evaluation.

Conclusions:

  • Decision Trees are highly effective for cardiovascular disease prediction, offering significant accuracy improvements.
  • The findings suggest that Decision Trees should be prioritized for developing reliable tools for early cardiovascular disease detection.
  • Further research may explore ensemble methods or feature engineering to enhance ANN and SVM performance in cardiovascular risk assessment.