A proactive intelligent model for identification of reduced factors affecting severity of coronary artery disease and its prediction

Aakash Bhattacharya; Riju Khatri; Tribikram Pradhan

doi:10.1109/ICACCI.2017.8126056

A proactive intelligent model for identification of reduced factors affecting severity of coronary artery disease and its prediction

Aakash Bhattacharya, Riju Khatri, Tribikram Pradhan

School of Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

Abstract

Obesity is an increasingly prevalent metabolic disorder, which results in increased risk of various diseases. One such disease is the coronary artery disease, which is the most common type of heart disease. Coronary artery disease (CAD) leads to the blockage of the arteries, that supply blood to the heart muscles, due to the accumulation of cholesterol and other material called plaque on the inner walls. This makes the arteries narrower and rigid thus restricting blood flow to the heart. In this paper, a model has been proposed to evaluate the severity of CAD among the different classes of obesity based on the prognostic markers and the various causative factors of CAD.

Original language	English
Title of host publication	2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1519-1524
Number of pages	6
Volume	2017-January
ISBN (Electronic)	9781509063673
DOIs	https://doi.org/10.1109/ICACCI.2017.8126056
Publication status	Published - 30-11-2017
Event	2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017 - Manipal, Mangalore, India Duration: 13-09-2017 → 16-09-2017

Publication series

Name	2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017
Volume	2017-January

Conference

Conference	2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017
Country/Territory	India
City	Manipal, Mangalore
Period	13-09-17 → 16-09-17

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Computer Science Applications
Information Systems

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/ICACCI.2017.8126056

Cite this

Bhattacharya, A., Khatri, R., & Pradhan, T. (2017). A proactive intelligent model for identification of reduced factors affecting severity of coronary artery disease and its prediction. In 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017 (Vol. 2017-January, pp. 1519-1524). (2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017; Vol. 2017-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICACCI.2017.8126056

Bhattacharya, Aakash ; Khatri, Riju ; Pradhan, Tribikram. / A proactive intelligent model for identification of reduced factors affecting severity of coronary artery disease and its prediction. 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017. Vol. 2017-January Institute of Electrical and Electronics Engineers Inc., 2017. pp. 1519-1524 (2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017).

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title = "A proactive intelligent model for identification of reduced factors affecting severity of coronary artery disease and its prediction",

abstract = "Obesity is an increasingly prevalent metabolic disorder, which results in increased risk of various diseases. One such disease is the coronary artery disease, which is the most common type of heart disease. Coronary artery disease (CAD) leads to the blockage of the arteries, that supply blood to the heart muscles, due to the accumulation of cholesterol and other material called plaque on the inner walls. This makes the arteries narrower and rigid thus restricting blood flow to the heart. In this paper, a model has been proposed to evaluate the severity of CAD among the different classes of obesity based on the prognostic markers and the various causative factors of CAD.",

author = "Aakash Bhattacharya and Riju Khatri and Tribikram Pradhan",

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Bhattacharya, A, Khatri, R & Pradhan, T 2017, A proactive intelligent model for identification of reduced factors affecting severity of coronary artery disease and its prediction. in 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017. vol. 2017-January, 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017, vol. 2017-January, Institute of Electrical and Electronics Engineers Inc., pp. 1519-1524, 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017, Manipal, Mangalore, India, 13-09-17. https://doi.org/10.1109/ICACCI.2017.8126056

A proactive intelligent model for identification of reduced factors affecting severity of coronary artery disease and its prediction. / Bhattacharya, Aakash; Khatri, Riju; Pradhan, Tribikram.
2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017. Vol. 2017-January Institute of Electrical and Electronics Engineers Inc., 2017. p. 1519-1524 (2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017; Vol. 2017-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - A proactive intelligent model for identification of reduced factors affecting severity of coronary artery disease and its prediction

AU - Bhattacharya, Aakash

AU - Khatri, Riju

AU - Pradhan, Tribikram

PY - 2017/11/30

Y1 - 2017/11/30

N2 - Obesity is an increasingly prevalent metabolic disorder, which results in increased risk of various diseases. One such disease is the coronary artery disease, which is the most common type of heart disease. Coronary artery disease (CAD) leads to the blockage of the arteries, that supply blood to the heart muscles, due to the accumulation of cholesterol and other material called plaque on the inner walls. This makes the arteries narrower and rigid thus restricting blood flow to the heart. In this paper, a model has been proposed to evaluate the severity of CAD among the different classes of obesity based on the prognostic markers and the various causative factors of CAD.

AB - Obesity is an increasingly prevalent metabolic disorder, which results in increased risk of various diseases. One such disease is the coronary artery disease, which is the most common type of heart disease. Coronary artery disease (CAD) leads to the blockage of the arteries, that supply blood to the heart muscles, due to the accumulation of cholesterol and other material called plaque on the inner walls. This makes the arteries narrower and rigid thus restricting blood flow to the heart. In this paper, a model has been proposed to evaluate the severity of CAD among the different classes of obesity based on the prognostic markers and the various causative factors of CAD.

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VL - 2017-January

T3 - 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017

SP - 1519

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BT - 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017

PB - Institute of Electrical and Electronics Engineers Inc.

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Bhattacharya A, Khatri R, Pradhan T. A proactive intelligent model for identification of reduced factors affecting severity of coronary artery disease and its prediction. In 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017. Vol. 2017-January. Institute of Electrical and Electronics Engineers Inc. 2017. p. 1519-1524. (2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017). doi: 10.1109/ICACCI.2017.8126056

A proactive intelligent model for identification of reduced factors affecting severity of coronary artery disease and its prediction

Abstract

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UN SDGs

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