Cardiovascular Disease Prediction using Various Machine Learning Algorithms
- 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.
DOI: https://doi.org/10.3844/jcssp.2022.993.1004
Copyright: © 2022 Debabrata Swain, Badal Parmar, Hansal Shah, Aditya Gandhi, Manas Ranjan Pradhan, Harprith Kaur and Biswaranjan Acharya. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
- 4,453 Views
- 1,500 Downloads
- 11 Citations
Download
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