Research Article Open Access

Cardiovascular Disease Prediction using Various Machine Learning Algorithms

Debabrata Swain1, Badal Parmar1, Hansal Shah1, Aditya Gandhi1, Manas Ranjan Pradhan2, Harprith Kaur3 and Biswaranjan Acharya4
  • 1 Department of Computer Science and Engineering, Pandit Deendayal Energy University, Gandhinagar, India
  • 2 Department of Information Technology, Skyline University College Sharjah, United Arab Emirates
  • 3 Faculty of Data Science and Information Technology, INTI International University, Nilai, Malaysia
  • 4 Department of Computer Engineering-AI, Marwadi University, Rajkot, India

Abstract

Almostone-third of all deaths caused around the world were caused due tocardiovascular diseases. Even if death was not the result, much cost isincurred during the treatment of such diseases. But much of these deaths andtreatments could have been prevented with prior action. Advance knowledge ofthe symptoms and consequently proper care can lead us to avoid such diseases.Thus, current research proposes a highly effective model to predict thepresence of heart diseases.  Bad eatinghabits, smoking, stress, and genetics are some of the factors that influenceour body mechanisms, which actually cause various irregularities in our heartsand thus adversely affect our bodies. The body mechanisms influenced byexternal factors have been included to prepare an efficient model to predictthe probability of cardiovascular diseases. UCI repository dataset has beenutilized for the training and testing purpose in our model. Then accordingly,five different algorithms namely Logistic Regression, Support Vector Machine,Multi-Layer Perceptron (MLP) Classifier with Principal Component Analysis(PCA), Deep Neural Network, Bootstrap Aggregation using Random Forests areexecuted on our filtered dataset to find which one is the optimum out of all ofthem. Pre-processing techniques have been extensively used to filter out thedataset. The data processing along with the different models employed make thisa sound paper, which could be utilized for real-world cases without any priormodification. Different places around the world would take different factorsinto account, hence our model can be used as it takes all critical factors fromseveral datasets.

Journal of Computer Science
Volume 18 No. 10, 2022, 993-1004

DOI: https://doi.org/10.3844/jcssp.2022.993.1004

Submitted On: 3 July 2022 Published On: 13 October 2022

How to Cite: Swain, D., Parmar, B., Shah, H., Gandhi, A., Pradhan, M. R., Kaur, H. & Acharya, B. (2022). Cardiovascular Disease Prediction using Various Machine Learning Algorithms. Journal of Computer Science, 18(10), 993-1004. https://doi.org/10.3844/jcssp.2022.993.1004

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Keywords

  • Cardiovascular Disease Prediction
  • Aggregated Dataset
  • Machine Learning Algorithms
  • Deep Learning
  • Bootstrap Aggregation using Random Forests
  • Logistic Regression
  • Deep Neural Network
  • MLP with PCA
  • SVC