Deployment of Heart Disease Prediction Model in Cloud Environment

• Author(s): I. Bhuvaneshwarri
• Paper ID: 1701742
• Page: 177-180
• Published Date: 08-07-2023
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 3 Issue 5 November-2019

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.

Citations

IRE Journals:
I. Bhuvaneshwarri "Deployment of Heart Disease Prediction Model in Cloud Environment" Iconic Research And Engineering Journals Volume 3 Issue 5 2019 Page 177-180

IEEE:
I. Bhuvaneshwarri "Deployment of Heart Disease Prediction Model in Cloud Environment" Iconic Research And Engineering Journals, vol. 3, no. 5, Nov. 2019

APA:
I. Bhuvaneshwarri (2019). Deployment of Heart Disease Prediction Model in Cloud Environment. Iconic Research And Engineering Journals, 3(5).

MLA:
I. Bhuvaneshwarri "Deployment of Heart Disease Prediction Model in Cloud Environment" Iconic Research And Engineering Journals, vol. 3, no. 5, Nov. 2019.

BibTeX

@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}
}