Cardiovascular Disease Detection Based on Interpretable and Explainable AI

Nitten Singh Rajjliwal; Girija Chetty

doi:10.1109/CSDE56538.2022.10089349

Cardiovascular Disease Detection Based on Interpretable and Explainable AI

Nitten Singh Rajjliwal
, Girija Chetty

Information Systems

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

This paper presents details of studies conducted to investigate interpretable and explainable machine learning and AI models for cardiovascular disease detection based on the publicly available Cleveland dataset. The study involves evaluating the interpretability and explainability capabilities of tradition shallow machine learning models and their potential for implementation under low resource settings, with limited training data available for model building, as compared to high performing deep learning models, requiring massive training datasets.

Original language	English
Title of host publication	Proceedings of IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2022
Place of Publication	United States
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	1-7
Number of pages	7
ISBN (Electronic)	9781665453059
DOIs	https://doi.org/10.1109/CSDE56538.2022.10089349
Publication status	Published - 2022
Event	2022 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2022 - Gold Coast, Australia Duration: 18 Dec 2022 → 20 Dec 2022

Publication series

Name	Proceedings of IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2022

Conference

Conference	2022 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2022
Country/Territory	Australia
City	Gold Coast
Period	18/12/22 → 20/12/22

UN SDGs

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

SDG 3 Good Health and Well-being
SDG 9 Industry, Innovation, and Infrastructure

Access to Document

10.1109/CSDE56538.2022.10089349

Cite this

Rajjliwal, N. S., & Chetty, G. (2022). Cardiovascular Disease Detection Based on Interpretable and Explainable AI. In Proceedings of IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2022 (pp. 1-7). (Proceedings of IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2022). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/CSDE56538.2022.10089349

Rajjliwal, Nitten Singh ; Chetty, Girija. / Cardiovascular Disease Detection Based on Interpretable and Explainable AI. Proceedings of IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2022. United States : IEEE, Institute of Electrical and Electronics Engineers, 2022. pp. 1-7 (Proceedings of IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2022).

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title = "Cardiovascular Disease Detection Based on Interpretable and Explainable AI",

abstract = "This paper presents details of studies conducted to investigate interpretable and explainable machine learning and AI models for cardiovascular disease detection based on the publicly available Cleveland dataset. The study involves evaluating the interpretability and explainability capabilities of tradition shallow machine learning models and their potential for implementation under low resource settings, with limited training data available for model building, as compared to high performing deep learning models, requiring massive training datasets.",

keywords = "cardiovascular disease, explainable AI, interpretable machine learning",

author = "Rajjliwal, \{Nitten Singh\} and Girija Chetty",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2022 ; Conference date: 18-12-2022 Through 20-12-2022",

year = "2022",

doi = "10.1109/CSDE56538.2022.10089349",

language = "English",

series = "Proceedings of IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2022",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

pages = "1--7",

booktitle = "Proceedings of IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2022",

address = "United States",

}

Rajjliwal, NS & Chetty, G 2022, Cardiovascular Disease Detection Based on Interpretable and Explainable AI. in Proceedings of IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2022. Proceedings of IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2022, IEEE, Institute of Electrical and Electronics Engineers, United States, pp. 1-7, 2022 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2022, Gold Coast, Australia, 18/12/22. https://doi.org/10.1109/CSDE56538.2022.10089349

Cardiovascular Disease Detection Based on Interpretable and Explainable AI. / Rajjliwal, Nitten Singh; Chetty, Girija.
Proceedings of IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2022. United States: IEEE, Institute of Electrical and Electronics Engineers, 2022. p. 1-7 (Proceedings of IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2022).

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

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T1 - Cardiovascular Disease Detection Based on Interpretable and Explainable AI

AU - Rajjliwal, Nitten Singh

AU - Chetty, Girija

PY - 2022

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N2 - This paper presents details of studies conducted to investigate interpretable and explainable machine learning and AI models for cardiovascular disease detection based on the publicly available Cleveland dataset. The study involves evaluating the interpretability and explainability capabilities of tradition shallow machine learning models and their potential for implementation under low resource settings, with limited training data available for model building, as compared to high performing deep learning models, requiring massive training datasets.

AB - This paper presents details of studies conducted to investigate interpretable and explainable machine learning and AI models for cardiovascular disease detection based on the publicly available Cleveland dataset. The study involves evaluating the interpretability and explainability capabilities of tradition shallow machine learning models and their potential for implementation under low resource settings, with limited training data available for model building, as compared to high performing deep learning models, requiring massive training datasets.

KW - cardiovascular disease

KW - explainable AI

KW - interpretable machine learning

UR - https://www.scopus.com/pages/publications/85153672875

UR - https://ieeexplore.ieee.org/xpl/conhome/10089231/proceeding

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DO - 10.1109/CSDE56538.2022.10089349

M3 - Conference contribution

AN - SCOPUS:85153672875

T3 - Proceedings of IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2022

SP - 1

EP - 7

BT - Proceedings of IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2022

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - United States

T2 - 2022 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2022

Y2 - 18 December 2022 through 20 December 2022

ER -

Rajjliwal NS, Chetty G. Cardiovascular Disease Detection Based on Interpretable and Explainable AI. In Proceedings of IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2022. United States: IEEE, Institute of Electrical and Electronics Engineers. 2022. p. 1-7. (Proceedings of IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2022). doi: 10.1109/CSDE56538.2022.10089349

Cardiovascular Disease Detection Based on Interpretable and Explainable AI

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