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Deployment of Heart Disease Prediction Model in Cloud Environment
Subject area: Science,Engineering and Technology · Area of research: Cloud Environment
Abstract
The main reason for heart failure is Cardiovascular Diseases (CVDs). The dataset used in this paper contains 9 attributes that can be used to predict death or mortality by heart failure. In this paper, a prediction modelin cloud environment is built to display the prediction outcome of the heart failure. The cloud service automatically generated the effective heart disease prediction model using pipeline-based approach. In this proposed work, Snap Random Forest Classifier is selected as the effective heart disease prediction model among other 7 prediction models with classification accuracy of 87.3%. The primary objective of this effective heart disease prediction model is to determine whether a patient should be diagnosed with heart disease or not, which is a binary outcome either 0 or 1. The outcome of binary value 1 implies that the patient will be diagnosed with heart disease and outcome of binary value 0 implies that the patient will not be diagnosed with heart disease.
References
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[3] Patel J, TejalUpadhyay D, Patel S. Heart disease prediction using machine learning and data mining technique. Heart Disease. 2015 Sep;7(1):129-37.
[4] Jagtap A, Malewadkar P, Baswat O, Rambade H. Heart disease prediction using machine learning. International Journal of Research in Engineering, Science and Management. 2019 Feb;2(2):352-5.
[5] Khourdifi Y, Bahaj M. Heart disease prediction and classification using machine learning algorithms optimized by particle swarm optimization and ant colony optimization. International Journal of Intelligent Engineering and Systems. 2019 Feb;12(1):242-52.
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How to cite this paper
@article{1701742,
author = {I. Bhuvaneshwarri},
title = {Deployment of Heart Disease Prediction Model in Cloud Environment},
journal = {Iconic Research And Engineering Journals},
year = {2019},
volume = {3},
number = {5},
pages = {177-180},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1701742.pdf},
abstract = {The main reason for heart failure is Cardiovascular Diseases (CVDs). The dataset used in this paper contains 9 attributes that can be used to predict death or mortality by heart failure. In this paper, a prediction modelin cloud environment is built to display the prediction outcome of the heart failure. The cloud service automatically generated the effective heart disease prediction model using pipeline-based approach. In this proposed work, Snap Random Forest Classifier is selected as the effective heart disease prediction model among other 7 prediction models with classification accuracy of 87.3%. The primary objective of this effective heart disease prediction model is to determine whether a patient should be diagnosed with heart disease or not, which is a binary outcome either 0 or 1. The outcome of binary value 1 implies that the patient will be diagnosed with heart disease and outcome of binary value 0 implies that the patient will not be diagnosed with heart disease.},
month = {November},
}