Heart disease prediction based on machine learning algorithms

Research Article
Open access

Heart disease prediction based on machine learning algorithms

Mingyuan Xu 1*
  • 1 National University of Singapore, Singapore, 119077, Singapore    
  • *corresponding author e0950171@u.nus.edu
Published on 14 June 2023 | https://doi.org/10.54254/2755-2721/6/20230959
ACE Vol.6
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-59-1
ISBN (Online): 978-1-915371-60-7

Abstract

Heart disease is a medical research field in which the outcome can benefit lots of people. Because there are several factors that might raise the risk of heart disease, it is useful to build a prediction model to assist people in assessing their health. This paper makes use of a Kaggle dataset that was derived from CDC (Centers for Disease Control and Prevention). First, 8 components are analyzed using diagrams, and then the dataset is used to train classifiers in machine learning models. This paper conducts a comparative study between different algorithms, including Decision Tree, Logistic Regression, SVM (Support Vector Machine), and Random Forest. Besides, the factors taken into consideration while evaluating performance include accuracy, precision, recall, and f1-score. As a result, the maximum accuracy is reached by SVM with a linear kernel, and logistic regression achieves the highest precision. In addition, the highest recall and f1-score are obtained from the model SVM with an RBF kernel.

Keywords:

Heart Disease Prediction, Decision Tree, Logistic Regression, SVM (Support Vector Machine), Random Forest

Xu,M. (2023). Heart disease prediction based on machine learning algorithms. Applied and Computational Engineering,6,790-798.
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References

[1]. Rajdhan, A., Agarwal, A., Sai, M., Ravi, D., Ghuli, P. Heart disease prediction using machine learning. International Journal of Research and Technology 9(04), 659-662 (2020).

[2]. Jagtap, A., Malewadkar P, Baswat, O., Rambade H. Heart disease prediction using machine learning. International Journal of Research in Engineering. Science and Management 2(2), 352-355 (2019).

[3]. Nagaraj, M. L., Chethan, C., Basavaraj, S. P. Prediction of heart disease using machine learning. International Journal of Recent Technology and Engineering 8(2), 474-477 (2019).

[4]. Chang, V., Ganatra, M. A., Hall, K., Golightly, L., Xu, Q. W. A. An assessment of machine learning models and algorithms for early prediction and diagnosis of diabetes using health indicators. Healthcare Analytics 2, 100118 (2022).

[5]. Chen, T. J. Simone A Ludwig, et al. A performance analysis of dimensionality reduction algorithms in machine learning models for cancer prediction. Healthcare Analytics 100125 (2022).

[6]. Javanmard, M. E., Ghaderi, S. F., Hoseinzadeh, M. Data mining with 12 machine learning algorithms for predict costs and carbon dioxide emission in integrated energy-water optimization model in buildings. Energy Conversion and Management 238, 114153 (2021).

[7]. Han J. W., Pei, J. Tong, H. H. Data mining: concepts and techniques. Morgan kaufmann (2022).

[8]. Hamrani, A. Akbarzadeh, A., Madramootoo, C. A. Machine learning for predicting greenhouse gas emissions from agricultural soils. Science of The Total Environment 741, 140338 (2020).

[9]. Wang, Z. Y., Wang, Y. R., Zeng, R., Srinivasan R. S., Ahrentzen, S. Random forest based hourly building energy prediction. Energy and Buildings 171, 11-25 (2018).

[10]. Ramalingam, V. V., Dandapath, A., Raja, M. K. Heart disease prediction using machine learning techniques: a survey. International Journal of Engineering & Technology 7(2.8), 684-687 (2018).

Cite this article

Xu,M. (2023). Heart disease prediction based on machine learning algorithms. Applied and Computational Engineering,6,790-798.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

ISBN：978-1-915371-59-1(Print) / 978-1-915371-60-7(Online)
Editor：Omer Burak Istanbullu
Conference website: http://www.confspml.org
Conference date: 25 February 2023
Series: Applied and Computational Engineering
Volume number: Vol.6
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Rajdhan, A., Agarwal, A., Sai, M., Ravi, D., Ghuli, P. Heart disease prediction using machine learning. International Journal of Research and Technology 9(04), 659-662 (2020).

[2]. Jagtap, A., Malewadkar P, Baswat, O., Rambade H. Heart disease prediction using machine learning. International Journal of Research in Engineering. Science and Management 2(2), 352-355 (2019).

[3]. Nagaraj, M. L., Chethan, C., Basavaraj, S. P. Prediction of heart disease using machine learning. International Journal of Recent Technology and Engineering 8(2), 474-477 (2019).

[4]. Chang, V., Ganatra, M. A., Hall, K., Golightly, L., Xu, Q. W. A. An assessment of machine learning models and algorithms for early prediction and diagnosis of diabetes using health indicators. Healthcare Analytics 2, 100118 (2022).

[5]. Chen, T. J. Simone A Ludwig, et al. A performance analysis of dimensionality reduction algorithms in machine learning models for cancer prediction. Healthcare Analytics 100125 (2022).

[6]. Javanmard, M. E., Ghaderi, S. F., Hoseinzadeh, M. Data mining with 12 machine learning algorithms for predict costs and carbon dioxide emission in integrated energy-water optimization model in buildings. Energy Conversion and Management 238, 114153 (2021).

[7]. Han J. W., Pei, J. Tong, H. H. Data mining: concepts and techniques. Morgan kaufmann (2022).

[8]. Hamrani, A. Akbarzadeh, A., Madramootoo, C. A. Machine learning for predicting greenhouse gas emissions from agricultural soils. Science of The Total Environment 741, 140338 (2020).

[9]. Wang, Z. Y., Wang, Y. R., Zeng, R., Srinivasan R. S., Ahrentzen, S. Random forest based hourly building energy prediction. Energy and Buildings 171, 11-25 (2018).

[10]. Ramalingam, V. V., Dandapath, A., Raja, M. K. Heart disease prediction using machine learning techniques: a survey. International Journal of Engineering & Technology 7(2.8), 684-687 (2018).

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