Heart Disease Prediction Using Machine Learning Techniques

Mohammed Khalid Hossen

doi:doi:10.11648/j.ajcst.20220503.11

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Heart Disease Prediction Using Machine Learning Techniques

Mohammed Khalid Hossen

Published in American Journal of Computer Science and Technology (Volume 5, Issue 3)

Received: 27 June 2022 Accepted: 13 July 2022 Published: 20 July 2022

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Abstract

Machine learning and artificial intelligence have been found useful in various disciplines during the course of their development, especially in the enormous increasing data in recent years. It can be more reliable for making better and faster decisions for disease predictions. So, machine learning algorithms are increasingly finding their application to predict various diseases. Constructing a model can also help us visualize and analyze diseases to improve reporting consistency and accuracy. This article has investigated how to detect heart disease by applying various machine learning algorithms. The study in this article has shown a two-step process. The heart disease dataset is first prepared into a required format for running through machine learning algorithms. Medical records and other information about patients are gathered from the UCI repository. The heart disease dataset is then used to determine whether or not the patients have heart disease. Secondly, Many valuable results are shown in this article. The accuracy rate of the machine learning algorithms, such as Logistic Regression, Support vector machine, K-Nearest-Neighbors, Random Forest, and Gradient Boosting Classifier, are validated through the confusion matrix. Current findings suggest that the Logistic Regression algorithm gives a high accuracy rate of 95% compared to other algorithms. It also shows high accuracy for f₁-score, recall, and precision than the other four different algorithms. However, increasing the accuracy rates to approximately 97% to 100% of the machine learning algorithms is the future study and challenging part of this research.

Published in	American Journal of Computer Science and Technology (Volume 5, Issue 3)
DOI	10.11648/j.ajcst.20220503.11
Page(s)	146-154
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 Science Publishing Group

Keywords

Machine Learning, Artificial Intelligence, Heart Disease, Linear Regression, Support Vector Machine, K-Nearest-Neighbors, Random Forest, Decision Tree, Gradient Boosting

References

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APA Style

Mohammed Khalid Hossen. (2022). Heart Disease Prediction Using Machine Learning Techniques. American Journal of Computer Science and Technology, 5(3), 146-154. https://doi.org/10.11648/j.ajcst.20220503.11

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ACS Style

Mohammed Khalid Hossen. Heart Disease Prediction Using Machine Learning Techniques. Am. J. Comput. Sci. Technol. 2022, 5(3), 146-154. doi: 10.11648/j.ajcst.20220503.11

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AMA Style

Mohammed Khalid Hossen. Heart Disease Prediction Using Machine Learning Techniques. Am J Comput Sci Technol. 2022;5(3):146-154. doi: 10.11648/j.ajcst.20220503.11

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@article{10.11648/j.ajcst.20220503.11,
  author = {Mohammed Khalid Hossen},
  title = {Heart Disease Prediction Using Machine Learning Techniques},
  journal = {American Journal of Computer Science and Technology},
  volume = {5},
  number = {3},
  pages = {146-154},
  doi = {10.11648/j.ajcst.20220503.11},
  url = {https://doi.org/10.11648/j.ajcst.20220503.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajcst.20220503.11},
  abstract = {Machine learning and artificial intelligence have been found useful in various disciplines during the course of their development, especially in the enormous increasing data in recent years. It can be more reliable for making better and faster decisions for disease predictions. So, machine learning algorithms are increasingly finding their application to predict various diseases. Constructing a model can also help us visualize and analyze diseases to improve reporting consistency and accuracy. This article has investigated how to detect heart disease by applying various machine learning algorithms. The study in this article has shown a two-step process. The heart disease dataset is first prepared into a required format for running through machine learning algorithms. Medical records and other information about patients are gathered from the UCI repository. The heart disease dataset is then used to determine whether or not the patients have heart disease. Secondly, Many valuable results are shown in this article. The accuracy rate of the machine learning algorithms, such as Logistic Regression, Support vector machine, K-Nearest-Neighbors, Random Forest, and Gradient Boosting Classifier, are validated through the confusion matrix. Current findings suggest that the Logistic Regression algorithm gives a high accuracy rate of 95% compared to other algorithms. It also shows high accuracy for f1-score, recall, and precision than the other four different algorithms. However, increasing the accuracy rates to approximately 97% to 100% of the machine learning algorithms is the future study and challenging part of this research.},
 year = {2022}
}

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TY - JOUR
T1 - Heart Disease Prediction Using Machine Learning Techniques
AU - Mohammed Khalid Hossen
Y1 - 2022/07/20
PY - 2022
N1 - https://doi.org/10.11648/j.ajcst.20220503.11
DO - 10.11648/j.ajcst.20220503.11
T2 - American Journal of Computer Science and Technology
JF - American Journal of Computer Science and Technology
JO - American Journal of Computer Science and Technology
SP - 146
EP - 154
PB - Science Publishing Group
SN - 2640-012X
UR - https://doi.org/10.11648/j.ajcst.20220503.11
AB - Machine learning and artificial intelligence have been found useful in various disciplines during the course of their development, especially in the enormous increasing data in recent years. It can be more reliable for making better and faster decisions for disease predictions. So, machine learning algorithms are increasingly finding their application to predict various diseases. Constructing a model can also help us visualize and analyze diseases to improve reporting consistency and accuracy. This article has investigated how to detect heart disease by applying various machine learning algorithms. The study in this article has shown a two-step process. The heart disease dataset is first prepared into a required format for running through machine learning algorithms. Medical records and other information about patients are gathered from the UCI repository. The heart disease dataset is then used to determine whether or not the patients have heart disease. Secondly, Many valuable results are shown in this article. The accuracy rate of the machine learning algorithms, such as Logistic Regression, Support vector machine, K-Nearest-Neighbors, Random Forest, and Gradient Boosting Classifier, are validated through the confusion matrix. Current findings suggest that the Logistic Regression algorithm gives a high accuracy rate of 95% compared to other algorithms. It also shows high accuracy for f1-score, recall, and precision than the other four different algorithms. However, increasing the accuracy rates to approximately 97% to 100% of the machine learning algorithms is the future study and challenging part of this research.
VL - 5
IS - 3
ER -

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Author Information

Mohammed Khalid Hossen

Department of Computer Science and Engineering, Sylhet Agricultural University, Sylhet, Bangladesh

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APA Style

Mohammed Khalid Hossen. (2022). Heart Disease Prediction Using Machine Learning Techniques. American Journal of Computer Science and Technology, 5(3), 146-154. https://doi.org/10.11648/j.ajcst.20220503.11

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ACS Style

Mohammed Khalid Hossen. Heart Disease Prediction Using Machine Learning Techniques. Am. J. Comput. Sci. Technol. 2022, 5(3), 146-154. doi: 10.11648/j.ajcst.20220503.11

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AMA Style

Mohammed Khalid Hossen. Heart Disease Prediction Using Machine Learning Techniques. Am J Comput Sci Technol. 2022;5(3):146-154. doi: 10.11648/j.ajcst.20220503.11

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@article{10.11648/j.ajcst.20220503.11,
  author = {Mohammed Khalid Hossen},
  title = {Heart Disease Prediction Using Machine Learning Techniques},
  journal = {American Journal of Computer Science and Technology},
  volume = {5},
  number = {3},
  pages = {146-154},
  doi = {10.11648/j.ajcst.20220503.11},
  url = {https://doi.org/10.11648/j.ajcst.20220503.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajcst.20220503.11},
  abstract = {Machine learning and artificial intelligence have been found useful in various disciplines during the course of their development, especially in the enormous increasing data in recent years. It can be more reliable for making better and faster decisions for disease predictions. So, machine learning algorithms are increasingly finding their application to predict various diseases. Constructing a model can also help us visualize and analyze diseases to improve reporting consistency and accuracy. This article has investigated how to detect heart disease by applying various machine learning algorithms. The study in this article has shown a two-step process. The heart disease dataset is first prepared into a required format for running through machine learning algorithms. Medical records and other information about patients are gathered from the UCI repository. The heart disease dataset is then used to determine whether or not the patients have heart disease. Secondly, Many valuable results are shown in this article. The accuracy rate of the machine learning algorithms, such as Logistic Regression, Support vector machine, K-Nearest-Neighbors, Random Forest, and Gradient Boosting Classifier, are validated through the confusion matrix. Current findings suggest that the Logistic Regression algorithm gives a high accuracy rate of 95% compared to other algorithms. It also shows high accuracy for f1-score, recall, and precision than the other four different algorithms. However, increasing the accuracy rates to approximately 97% to 100% of the machine learning algorithms is the future study and challenging part of this research.},
 year = {2022}
}

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