Optimizing Heart Attack Detection with Brown-Bear Optimization Algorithm and CNN in Healthcare 4.0

Akshat Gaurav; Brij B. Gupta; Jinsong Wu; Varsha Arya; Priyanka Chaurasia; Kwok Tai Chui

doi:10.1109/gcwkshp64532.2024.11101658

Optimizing Heart Attack Detection with Brown-Bear Optimization Algorithm and CNN in Healthcare 4.0

Akshat Gaurav
, Brij B. Gupta
, Jinsong Wu
, Varsha Arya
, Priyanka Chaurasia
, Kwok Tai Chui

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

26 Downloads (Pure)

Abstract

Heart disease is a non-communicable decease that lead to death if not treated. In the world most of the people died due to heart disease because they are not treated on time or their disease is not detected at early stage. Due to this efficient heart disease prediction techniques are important for the development of Healthcare 4.0. However, most of the current heart disease detection algorithms are either complex or not optimizes for efficient hyper-parameters. In this context, we proposed a CNN based lightweight heart disease detection framework (trained in 5 epoch). We also used random forest algorithm to identify the most important feature and Brown-Bear Algorithm for optimization of the hyper-parameter of CNN. We also compared the proposed model with current literature and present the efficiency of our proposed framework.

Original language	English
Title of host publication	2024 IEEE Globecom Workshops (GC Wkshps)
Publisher	IEEE
Pages	1-6
Number of pages	6
ISBN (Electronic)	979-8-3315-0567-7
ISBN (Print)	979-8-3315-0568-4
DOIs	https://doi.org/10.1109/gcwkshp64532.2024.11101658
Publication status	Published online - 12 Aug 2025
Event	2024 IEEE Globecom Workshops (GC Wkshps) - Cape Town, South Africa Duration: 8 Dec 2024 → 12 Dec 2024

Publication series

Name	2024 IEEE Globecom Workshops (GC Wkshps)
Publisher	IEEE Control Society
ISSN (Print)	2166-0069
ISSN (Electronic)	2166-0077

Conference

Conference	2024 IEEE Globecom Workshops (GC Wkshps)
Country/Territory	South Africa
City	Cape Town
Period	8/12/24 → 12/12/24

Funding

The work described in this paper was fully supported by a grant from Hong Kong Metropolitan University(RD/2023/2.3).

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Heart Disease Detection
CNN
Brown-Bear Algorithm
Healthcare 4.0
Heart
Medical services
Cardiac arrest
Real-time systems
Smart devices
Optimization
Random forests
Tuning
Diseases
Context modeling

Access to Document

10.1109/gcwkshp64532.2024.11101658

a3-gaurav paperAuthor Accepted Manuscript, 969 KBLicence: Unspecified

Cite this

@inproceedings{adfee4a11a724d799c7ed691b5b3d50c,

title = "Optimizing Heart Attack Detection with Brown-Bear Optimization Algorithm and CNN in Healthcare 4.0",

abstract = "Heart disease is a non-communicable decease that lead to death if not treated. In the world most of the people died due to heart disease because they are not treated on time or their disease is not detected at early stage. Due to this efficient heart disease prediction techniques are important for the development of Healthcare 4.0. However, most of the current heart disease detection algorithms are either complex or not optimizes for efficient hyper-parameters. In this context, we proposed a CNN based lightweight heart disease detection framework (trained in 5 epoch). We also used random forest algorithm to identify the most important feature and Brown-Bear Algorithm for optimization of the hyper-parameter of CNN. We also compared the proposed model with current literature and present the efficiency of our proposed framework.",

keywords = "Heart Disease Detection, CNN, Brown-Bear Algorithm, Healthcare 4.0, Heart, Medical services, Cardiac arrest, Real-time systems, Smart devices, Optimization, Random forests, Tuning, Diseases, Context modeling",

author = "Akshat Gaurav and Gupta, \{Brij B.\} and Jinsong Wu and Varsha Arya and Priyanka Chaurasia and Chui, \{Kwok Tai\}",

year = "2025",

month = aug,

day = "12",

doi = "10.1109/gcwkshp64532.2024.11101658",

language = "English",

isbn = "979-8-3315-0568-4",

series = "2024 IEEE Globecom Workshops (GC Wkshps)",

publisher = "IEEE",

pages = "1--6",

booktitle = "2024 IEEE Globecom Workshops (GC Wkshps)",

address = "United States",

note = "2024 IEEE Globecom Workshops (GC Wkshps) ; Conference date: 08-12-2024 Through 12-12-2024",

}

Gaurav, A, Gupta, BB, Wu, J, Arya, V, Chaurasia, P & Chui, KT 2025, Optimizing Heart Attack Detection with Brown-Bear Optimization Algorithm and CNN in Healthcare 4.0. in 2024 IEEE Globecom Workshops (GC Wkshps). 2024 IEEE Globecom Workshops (GC Wkshps), IEEE, pp. 1-6, 2024 IEEE Globecom Workshops (GC Wkshps), Cape Town, South Africa, 8/12/24. https://doi.org/10.1109/gcwkshp64532.2024.11101658

TY - GEN

T1 - Optimizing Heart Attack Detection with Brown-Bear Optimization Algorithm and CNN in Healthcare 4.0

AU - Gaurav, Akshat

AU - Gupta, Brij B.

AU - Wu, Jinsong

AU - Arya, Varsha

AU - Chaurasia, Priyanka

AU - Chui, Kwok Tai

PY - 2025/8/12

Y1 - 2025/8/12

N2 - Heart disease is a non-communicable decease that lead to death if not treated. In the world most of the people died due to heart disease because they are not treated on time or their disease is not detected at early stage. Due to this efficient heart disease prediction techniques are important for the development of Healthcare 4.0. However, most of the current heart disease detection algorithms are either complex or not optimizes for efficient hyper-parameters. In this context, we proposed a CNN based lightweight heart disease detection framework (trained in 5 epoch). We also used random forest algorithm to identify the most important feature and Brown-Bear Algorithm for optimization of the hyper-parameter of CNN. We also compared the proposed model with current literature and present the efficiency of our proposed framework.

AB - Heart disease is a non-communicable decease that lead to death if not treated. In the world most of the people died due to heart disease because they are not treated on time or their disease is not detected at early stage. Due to this efficient heart disease prediction techniques are important for the development of Healthcare 4.0. However, most of the current heart disease detection algorithms are either complex or not optimizes for efficient hyper-parameters. In this context, we proposed a CNN based lightweight heart disease detection framework (trained in 5 epoch). We also used random forest algorithm to identify the most important feature and Brown-Bear Algorithm for optimization of the hyper-parameter of CNN. We also compared the proposed model with current literature and present the efficiency of our proposed framework.

KW - Heart Disease Detection

KW - CNN

KW - Brown-Bear Algorithm

KW - Healthcare 4.0

KW - Heart

KW - Medical services

KW - Cardiac arrest

KW - Real-time systems

KW - Smart devices

KW - Optimization

KW - Random forests

KW - Tuning

KW - Diseases

KW - Context modeling

UR - https://pure.ulster.ac.uk/en/publications/adfee4a1-1a72-4d79-9c7e-d691b5b3d50c

U2 - 10.1109/gcwkshp64532.2024.11101658

DO - 10.1109/gcwkshp64532.2024.11101658

M3 - Conference contribution

SN - 979-8-3315-0568-4

T3 - 2024 IEEE Globecom Workshops (GC Wkshps)

SP - 1

EP - 6

BT - 2024 IEEE Globecom Workshops (GC Wkshps)

PB - IEEE

T2 - 2024 IEEE Globecom Workshops (GC Wkshps)

Y2 - 8 December 2024 through 12 December 2024

ER -

Optimizing Heart Attack Detection with Brown-Bear Optimization Algorithm and CNN in Healthcare 4.0

Abstract

Publication series

Conference

Funding

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Cite this