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Identification of Cardiac Patients Based on the Medical Conditions Using Machine Learning Models

Kumar, Krishna LU orcid ; Kumar, Narendra ; Kumar, Aman ; Mohammed, Mazin Abed ; Al-Waisy, Alaa S. ; Jaber, Mustafa Musa ; Pandey, Neeraj Kumar ; Shah, Rachna ; Saini, Gaurav and Eid, Fatma , et al. (2022) In Computational Intelligence and Neuroscience 2022.
Abstract

Chronic diseases are the most severe health concern today, and heart disease is one of them. Coronary artery disease (CAD) affects blood flow to the heart, and it is the most common type of heart disease which causes a heart attack. High blood pressure, high cholesterol, and smoking significantly increase the risk of heart disease. To estimate the risk of heart disease is a complex process because it depends on various input parameters. The linear and analytical models failed due to their assumptions and limited dataset. The existing studies have used medical data for classification purposes, which help to identify the exact condition of the patient, but no one has developed any correlation equation which can be directly used to... (More)

Chronic diseases are the most severe health concern today, and heart disease is one of them. Coronary artery disease (CAD) affects blood flow to the heart, and it is the most common type of heart disease which causes a heart attack. High blood pressure, high cholesterol, and smoking significantly increase the risk of heart disease. To estimate the risk of heart disease is a complex process because it depends on various input parameters. The linear and analytical models failed due to their assumptions and limited dataset. The existing studies have used medical data for classification purposes, which help to identify the exact condition of the patient, but no one has developed any correlation equation which can be directly used to identify the patients. In this paper, mathematical models have been developed using the medical database of patients suffering from heart disease. Curve fitting and artificial neural network (ANN) have been applied to model the condition of patients to find out whether the patient is suffering from heart disease or not. The developed curve fitting model can identify the cardiac patient with accuracy, having a coefficient of determination (R2-value) of 0.6337 and mean absolute error (MAE) of 0.293 at a root mean square error (RMSE) of 0.3688, and the ANN-based model can identify the cardiac patient with accuracy having a coefficient of determination (R2-value) of 0.8491 and MAE of 0.20 at RMSE of 0.267, it has been found that ANN provides superior mathematical modeling than curve fitting method in identifying the heart disease patients. Medical professionals can utilize this model to identify heart patients without any angiography or computed tomography angiography test.

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publishing date
type
Contribution to journal
publication status
published
subject
in
Computational Intelligence and Neuroscience
volume
2022
article number
5882144
publisher
Hindawi Limited
external identifiers
  • scopus:85135209722
  • pmid:35909858
ISSN
1687-5265
DOI
10.1155/2022/5882144
language
English
LU publication?
no
additional info
Publisher Copyright: © 2022 Krishna Kumar et al.
id
6bb14651-d04d-4b51-97ca-d04123ac4039
date added to LUP
2024-04-15 12:58:35
date last changed
2024-08-05 23:55:00
@article{6bb14651-d04d-4b51-97ca-d04123ac4039,
  abstract     = {{<p>Chronic diseases are the most severe health concern today, and heart disease is one of them. Coronary artery disease (CAD) affects blood flow to the heart, and it is the most common type of heart disease which causes a heart attack. High blood pressure, high cholesterol, and smoking significantly increase the risk of heart disease. To estimate the risk of heart disease is a complex process because it depends on various input parameters. The linear and analytical models failed due to their assumptions and limited dataset. The existing studies have used medical data for classification purposes, which help to identify the exact condition of the patient, but no one has developed any correlation equation which can be directly used to identify the patients. In this paper, mathematical models have been developed using the medical database of patients suffering from heart disease. Curve fitting and artificial neural network (ANN) have been applied to model the condition of patients to find out whether the patient is suffering from heart disease or not. The developed curve fitting model can identify the cardiac patient with accuracy, having a coefficient of determination (R2-value) of 0.6337 and mean absolute error (MAE) of 0.293 at a root mean square error (RMSE) of 0.3688, and the ANN-based model can identify the cardiac patient with accuracy having a coefficient of determination (R2-value) of 0.8491 and MAE of 0.20 at RMSE of 0.267, it has been found that ANN provides superior mathematical modeling than curve fitting method in identifying the heart disease patients. Medical professionals can utilize this model to identify heart patients without any angiography or computed tomography angiography test.</p>}},
  author       = {{Kumar, Krishna and Kumar, Narendra and Kumar, Aman and Mohammed, Mazin Abed and Al-Waisy, Alaa S. and Jaber, Mustafa Musa and Pandey, Neeraj Kumar and Shah, Rachna and Saini, Gaurav and Eid, Fatma and Al-Andoli, Mohammed Nasser}},
  issn         = {{1687-5265}},
  language     = {{eng}},
  publisher    = {{Hindawi Limited}},
  series       = {{Computational Intelligence and Neuroscience}},
  title        = {{Identification of Cardiac Patients Based on the Medical Conditions Using Machine Learning Models}},
  url          = {{http://dx.doi.org/10.1155/2022/5882144}},
  doi          = {{10.1155/2022/5882144}},
  volume       = {{2022}},
  year         = {{2022}},
}