Intelligent cardiovascular disease diagnosis using deep learning enhanced neural network with ant colony optimization

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F24%3A50021767" target="_blank" >RIV/62690094:18470/24:50021767 - isvavai.cz</a>

Result on the web

<a href="https://www.nature.com/articles/s41598-024-71932-z" target="_blank" >https://www.nature.com/articles/s41598-024-71932-z</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1038/s41598-024-71932-z" target="_blank" >10.1038/s41598-024-71932-z</a>

Result language

angličtina

Original language name

Intelligent cardiovascular disease diagnosis using deep learning enhanced neural network with ant colony optimization

Original language description

To identify patterns in big medical datasets and use Deep Learning and Machine Learning (ML) to reliably diagnose Cardio Vascular Disease (CVD), researchers are currently delving deeply into these fields. Training on large datasets and producing highly accurate validation results is exceedingly difficult. Furthermore, early and precise diagnosis is necessary due to the increased global prevalence of cardiovascular disease (CVD). However, the increasing complexity of healthcare datasets makes it challenging to detect feature connections and produce precise predictions. To address these issues, the Intelligent Cardiovascular Disease Diagnosis based on Ant Colony Optimisation with Enhanced Deep Learning (ICVD-ACOEDL) model was developed. This model employs feature selection (FS) and hyperparameter optimization to diagnose CVD. Applying a min–max scaler, medical data is first consistently prepared. The key feature that sets ICVD-ACOEDL apart is the use of Ant Colony Optimisation (ACO) to select an optimal feature subset, which in turn helps to upgrade the performance of the ensuring deep learning enhanced neural network (DLENN) classifier. The model reforms the hyperparameters of DLENN for CVD classification using Bayesian optimization. Comprehensive evaluations on benchmark medical datasets show that ICVD-ACOEDL exceeds existing techniques, indicating that it could have a significant impact on CVD diagnosis. The model furnishes a workable way to increase CVD classification efficiency and accuracy in real-world medical situations by incorporating ACO for feature selection, min–max scaling for data pre-processing, and Bayesian optimization for hyperparameter tweaking.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Scientific reports

ISSN

2045-2322

e-ISSN

2045-2322

Volume of the periodical

14

Issue of the periodical within the volume

1

Country of publishing house

DE - GERMANY

Number of pages

16

Pages from-to

"Article Number: 21777"

UT code for WoS article

001322528400036

EID of the result in the Scopus database

2-s2.0-85204299385