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Article Open Access December 17, 2024

An Analysis of Performance and Comparison of Models for Cardiovascular Disease Prediction via Machine Learning Models in Healthcare

Anand Polamarasetti 1,*, Krishna Madhav Jha 2, Vasu Velaga 3, Kishan Kumar Routhu 4, Gangadhar Sadaram 5, Suneel Babu Boppana 6 and Srikanth Reddy Vangala 7
1
MCA, Andhra University, USA
2
Topbuild Corp, Sr Business Analyst, USA
3
Cintas Corporation, SAP Functional Analyst, USA
4
ADP, Senior Solution Architect, USA
5
Bank of America, VP DevOps/ OpenShift Admin Engineer, USA
6
iSite Technologies, Project Manager, USA
7
Department of Computer Science, University of Bridgeport, USA
Publihed in: Current Research in Public Health (Volume 4, Issue 2, 2024)
Page(s): 96-108
DOI: 10.31586/jaibd.2024.1332
Received
August 02, 2024
Revised
September 27, 2024
Accepted
November 20, 2024
Published
December 17, 2024
Keywords
Cardiovascular Disease; Heart Disease Classification; Convolutional Neural Network (CNN); UCI Heart Disease Dataset; Sigmoid Function; Machine Learning (ML); Deep Learning (DL)
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright: Copyright © The Author(s), 2024. Published by Scientific Publications
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Cite This Article

APA Style
Polamarasetti, A. , Polamarasetti, A. Jha, K. M. , Jha, K. M. Velaga, V. , Velaga, V. Routhu, K. K. , Routhu, K. K. Sadaram, G. , Sadaram, G. Boppana, S. B. , & Boppana, S. B. (2024). An Analysis of Performance and Comparison of Models for Cardiovascular Disease Prediction via Machine Learning Models in Healthcare. Current Research in Public Health, 4(2), 96-108. https://doi.org/10.31586/jaibd.2024.1332
ACS Style
Polamarasetti, A. ; Polamarasetti, A. Jha, K. M. ; Jha, K. M. Velaga, V. ; Velaga, V. Routhu, K. K. ; Routhu, K. K. Sadaram, G. ; Sadaram, G. Boppana, S. B. ; Boppana, S. B. An Analysis of Performance and Comparison of Models for Cardiovascular Disease Prediction via Machine Learning Models in Healthcare. Current Research in Public Health 2024 4(2), 96-108. https://doi.org/10.31586/jaibd.2024.1332
Chicago/Turabian Style
Polamarasetti, Anand, Anand Polamarasetti. Krishna Madhav Jha, Krishna Madhav Jha. Vasu Velaga, Vasu Velaga. Kishan Kumar Routhu, Kishan Kumar Routhu. Gangadhar Sadaram, Gangadhar Sadaram. Suneel Babu Boppana, and Suneel Babu Boppana. 2024. "An Analysis of Performance and Comparison of Models for Cardiovascular Disease Prediction via Machine Learning Models in Healthcare". Current Research in Public Health 4, no. 2: 96-108. https://doi.org/10.31586/jaibd.2024.1332
AMA Style
Polamarasetti A, Polamarasetti AJha KM, Jha KMVelaga V, Velaga VRouthu KK, Routhu KKSadaram G, Sadaram GBoppana SB, Boppana SB. An Analysis of Performance and Comparison of Models for Cardiovascular Disease Prediction via Machine Learning Models in Healthcare. Current Research in Public Health. 2024; 4(2):96-108. https://doi.org/10.31586/jaibd.2024.1332 
@Article{crph1332,
AUTHOR = {Polamarasetti, Anand and Jha, Krishna Madhav and Velaga, Vasu and Routhu, Kishan Kumar and Sadaram, Gangadhar and Boppana, Suneel Babu and Vangala, Srikanth Reddy},
TITLE = {An Analysis of Performance and Comparison of Models for Cardiovascular Disease Prediction via Machine Learning Models in Healthcare},
JOURNAL = {Current Research in Public Health},
VOLUME = {4},
YEAR = {2024},
NUMBER = {2},
PAGES = {96-108},
URL = {https://www.scipublications.com/journal/index.php/JAIBD/article/view/1332},
ISSN = {2831-5162},
DOI = {10.31586/jaibd.2024.1332},
ABSTRACT = {Over the past few decades, cardiovascular disease and related complications have surpassed all others as the important causes of death on a universal scale. At the moment, they are the important cause of mortality universal, including in India. It is important to know how to find cardiovascular problems early so that patients get better care and prices go down. This project utilizes the UCI Heart Disease Dataset to develop ML and DL models capable of detecting cardiac diseases. Heart illness was categorized using Convolutional Neural Network (CNN) models, which are able to detect intricate patterns in supplied data. A confusion matrix rating, an F1-score, a ROC curve, accuracy, precision, and recall were some of the measures used to grade the model. It did much better than the Neural Network, Deep Neural Network (DNN), and Gradient Boosted Trees (GBT) models, with 91.71% accuracy, 88.88% precision, 82.75% memory, and 85.70% F1-score. Comparative study showed that CNN was the most accurate model. Other models had different balances between accuracy and recall. The experiment results show that the optional CNN model is a decent way to identify cardiovascular disease. This means that it could be used in healthcare systems to find diseases earlier and treat patients better.},
}
%0 Journal Article
%A Polamarasetti, Anand
%A Jha, Krishna Madhav
%A Velaga, Vasu
%A Routhu, Kishan Kumar
%A Sadaram, Gangadhar
%A Boppana, Suneel Babu
%A Vangala, Srikanth Reddy
%D 2024
%J Current Research in Public Health

%@ 2831-5162
%V 4
%N 2
%P 96-108

%T An Analysis of Performance and Comparison of Models for Cardiovascular Disease Prediction via Machine Learning Models in Healthcare
%M doi:10.31586/jaibd.2024.1332
%U https://www.scipublications.com/journal/index.php/JAIBD/article/view/1332

TY  - JOUR
AU  - Polamarasetti, Anand
AU  - Jha, Krishna Madhav
AU  - Velaga, Vasu
AU  - Routhu, Kishan Kumar
AU  - Sadaram, Gangadhar
AU  - Boppana, Suneel Babu
AU  - Vangala, Srikanth Reddy
TI  - An Analysis of Performance and Comparison of Models for Cardiovascular Disease Prediction via Machine Learning Models in Healthcare
T2  - Current Research in Public Health
PY  - 2024
VL  - 4
IS  - 2
SN  - 2831-5162
SP  - 96
EP  - 108
UR  - https://www.scipublications.com/journal/index.php/JAIBD/article/view/1332
AB  - Over the past few decades, cardiovascular disease and related complications have surpassed all others as the important causes of death on a universal scale. At the moment, they are the important cause of mortality universal, including in India. It is important to know how to find cardiovascular problems early so that patients get better care and prices go down. This project utilizes the UCI Heart Disease Dataset to develop ML and DL models capable of detecting cardiac diseases. Heart illness was categorized using Convolutional Neural Network (CNN) models, which are able to detect intricate patterns in supplied data. A confusion matrix rating, an F1-score, a ROC curve, accuracy, precision, and recall were some of the measures used to grade the model. It did much better than the Neural Network, Deep Neural Network (DNN), and Gradient Boosted Trees (GBT) models, with 91.71% accuracy, 88.88% precision, 82.75% memory, and 85.70% F1-score. Comparative study showed that CNN was the most accurate model. Other models had different balances between accuracy and recall. The experiment results show that the optional CNN model is a decent way to identify cardiovascular disease. This means that it could be used in healthcare systems to find diseases earlier and treat patients better.
DO  - An Analysis of Performance and Comparison of Models for Cardiovascular Disease Prediction via Machine Learning Models in Healthcare
TI  - 10.31586/jaibd.2024.1332
ER  -