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Learning-based techniques for heart disease prediction: a survey of models and performance metrics

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Abstract

Heart disease (HD) is a major threat to human health, and the medical field generates vast amounts of data that doctors struggle to effectively interpret and use. Early prediction and classification of HD types are crucial for effective medical treatment. Researchers have found it important to use learning-based techniques from machine and deep learning, such as supervised and deep neural networks, to develop automatic models for HD. These techniques have been used to simulate HD management and extract important features from complex data sets. This survey examines various HD prediction models, classifying the learning-based techniques, datasets, and contexts used, and analyzing the performance metrics of each contribution. It also clarifies which method suits a type of HD. With the growth of data sets, researchers are increasingly utilizing these techniques to create more precise models. However, there is still much work to be done to improve the accuracy of HD predictions.

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Data Availibility Statement

Data sharing is not applicable to this article as no new data were generated or analyzed during the current study.

Abbreviations

ACO:

Ant Colony Optimization

AI:

Artificial Intelligence

AL:

Active Learning

ANN:

Artificial Neural Network

AUC:

Area Under Curve

CA:

Cardiac Arrhythmia

CAD:

Coronary Artery Disease

CART:

Classification and Regression Trees

CHD:

Coronary Heart Disease

CM:

Cardiomyopathy

CNHD:

Congenital Heart Disease

CVD:

Cardiovascular Diseases

DBSCAN:

Density-Based Spatial Clustering of Applications with Noise

DL:

Deep Learning

DLT:

Deep Learning Techniques

DT:

Decision Trees

EL:

Evolutionary Learning

FL:

Fuzzy Logic

FS:

Feature Selection

GA:

Genetic Algorithm

GB:

Gradient Boosting

HD:

Heart Disease

HF:

Heart Failure

ISO DATA:

Iterative Self-Organizing Data

KM:

K-Means

KNN:

K-Nearest Neighbor

LBTs:

Learning-Based Techniques

LDA:

Latent Dirichlet Allocation

LIMBO:

ScaLable InforMation Bottleneck

LiR:

Linear Regression

LR :

Logistic Regression

LSTM:

Long Short-Term Memory

MI:

Myocardial Infarction

ML:

Machine Learning

MLP:

Multi Layer Perceptron

NB:

Naive Bayes

NN:

Neural Network

PAD:

Peripheral Artery Disease

PCA:

Principal Component Analysis

PD:

Pericardial Disease

PSO:

Particle Swarm Optimization

RBFN:

Radial Basis Function Network

RFA:

Random Forest Algorithm

RHD:

Rheumatic Heart Disease

RL:

Reinforcement Learning

RNN:

Recurrent Neural Network

ROC:

Receiver Operating Characteristic Curve

ROCK:

Chameleon Robust Clustering using links

SHD:

Structural Heart Disease

SL:

Supervised Learning

SMOTE:

Synthetic Minority Over-Sampling Technique

SSL:

Semi-Supervised Learning

SVM:

Support Vector Machine

UCI:

University of California Irvine

USL:

Unsupervised Learning

VD:

Vascular Disease

XGB:

Extreme Gradient Boosting

AD:

Anomaly Detection

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  124. ** Zhang

  125. EIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh, 11586, Saudi Arabia

    Muhammad Asim, Ahmed A. Abd El-Latif & Mohamed Hammad

  126. School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, 510006, China

    Muhammad Asim

  127. Department of Mathematics and Computer Science, Faculty of Science, Menoufia University, Shebin El-Koom, 32511, Egypt

    Ahmed A. Abd El-Latif

  128. Information Technology Department, Faculty of Computers and Information, Menoufia University, Shibin El Kom, 32511, Egypt

    Mohamed Hammad

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  1. Pierre Claver Bizimana
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Bizimana, P.C., Zhang, Z., Asim, M. et al. Learning-based techniques for heart disease prediction: a survey of models and performance metrics. Multimed Tools Appl 83, 39867–39921 (2024). https://doi.org/10.1007/s11042-023-17051-9

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