A Data-Driven Approach to Cardiac Health: Machine Learning for Heart Disease Prediction

Jeel Talaviya; Dhruv Trivedi; Ronish Ramani; Anjali Diwan; Rajesh Mahadeva; Rajesh Mahadeva

doi:10.1109/TEMSCON-ASPAC59527.2023.10531380

A Data-Driven Approach to Cardiac Health: Machine Learning for Heart Disease Prediction

Jeel Talaviya
, Dhruv Trivedi
, Ronish Ramani
, Anjali Diwan
, Rajesh Mahadeva
, Rajesh Mahadeva

Physics

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

1 Scopus citations

Abstract

Heart disease remains one of the leading causes of death worldwide, underscoring the critical need for accurate and timely prediction models. The outcome of cardiac disease is one area where machine learning algorithms have shown substantial potential. A rapidly advancing area of research is focused on using machine learning for heart disease prediction. Recent studies have extensively explored machine learning methods to anticipate heart disease in patients. This research aims to develop precise prediction models that can identify individuals at high risk of developing heart disease. These models consider various characteristics such as age, gender, medical history, and lifestyle choices to calculate the likelihood of heart disease. Notably, the accuracy of these machine learning models often surpasses that of traditional methods used for predicting cardiac disease. Integrating machine learning algorithms into heart disease diagnosis and treatment can improve patient outcomes and overall health.

Original language	British English
Title of host publication	Proceedings of 2023 IEEE Technology and Engineering Management Conference - Asia Pacific, TEMSCON-ASPAC 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350384659
DOIs	https://doi.org/10.1109/TEMSCON-ASPAC59527.2023.10531380
State	Published - 2023
Event	2023 IEEE Technology and Engineering Management Conference - Asia Pacific, TEMSCON-ASPAC 2023 - Bengaluru, India Duration: 15 Dec 2023 → 16 Dec 2023

Publication series

Name	Proceedings of 2023 IEEE Technology and Engineering Management Conference - Asia Pacific, TEMSCON-ASPAC 2023

Conference

Conference	2023 IEEE Technology and Engineering Management Conference - Asia Pacific, TEMSCON-ASPAC 2023
Country/Territory	India
City	Bengaluru
Period	15/12/23 → 16/12/23

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

Classification Algorithms
Heart Disease Prediction
Logistic Regression (LR)
Machine Learning
Predictive Modeling

Access to Document

10.1109/TEMSCON-ASPAC59527.2023.10531380

OpenUrl availability

Full text

Cite this

Talaviya, J., Trivedi, D., Ramani, R., Diwan, A., Mahadeva, R., & Mahadeva, R. (2023). A Data-Driven Approach to Cardiac Health: Machine Learning for Heart Disease Prediction. In Proceedings of 2023 IEEE Technology and Engineering Management Conference - Asia Pacific, TEMSCON-ASPAC 2023 (Proceedings of 2023 IEEE Technology and Engineering Management Conference - Asia Pacific, TEMSCON-ASPAC 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/TEMSCON-ASPAC59527.2023.10531380

Talaviya, Jeel ; Trivedi, Dhruv ; Ramani, Ronish et al. / A Data-Driven Approach to Cardiac Health : Machine Learning for Heart Disease Prediction. Proceedings of 2023 IEEE Technology and Engineering Management Conference - Asia Pacific, TEMSCON-ASPAC 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (Proceedings of 2023 IEEE Technology and Engineering Management Conference - Asia Pacific, TEMSCON-ASPAC 2023).

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abstract = "Heart disease remains one of the leading causes of death worldwide, underscoring the critical need for accurate and timely prediction models. The outcome of cardiac disease is one area where machine learning algorithms have shown substantial potential. A rapidly advancing area of research is focused on using machine learning for heart disease prediction. Recent studies have extensively explored machine learning methods to anticipate heart disease in patients. This research aims to develop precise prediction models that can identify individuals at high risk of developing heart disease. These models consider various characteristics such as age, gender, medical history, and lifestyle choices to calculate the likelihood of heart disease. Notably, the accuracy of these machine learning models often surpasses that of traditional methods used for predicting cardiac disease. Integrating machine learning algorithms into heart disease diagnosis and treatment can improve patient outcomes and overall health.",

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author = "Jeel Talaviya and Dhruv Trivedi and Ronish Ramani and Anjali Diwan and Rajesh Mahadeva and Rajesh Mahadeva",

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Talaviya, J, Trivedi, D, Ramani, R, Diwan, A, Mahadeva, R & Mahadeva, R 2023, A Data-Driven Approach to Cardiac Health: Machine Learning for Heart Disease Prediction. in Proceedings of 2023 IEEE Technology and Engineering Management Conference - Asia Pacific, TEMSCON-ASPAC 2023. Proceedings of 2023 IEEE Technology and Engineering Management Conference - Asia Pacific, TEMSCON-ASPAC 2023, Institute of Electrical and Electronics Engineers Inc., 2023 IEEE Technology and Engineering Management Conference - Asia Pacific, TEMSCON-ASPAC 2023, Bengaluru, India, 15/12/23. https://doi.org/10.1109/TEMSCON-ASPAC59527.2023.10531380

A Data-Driven Approach to Cardiac Health: Machine Learning for Heart Disease Prediction. / Talaviya, Jeel; Trivedi, Dhruv; Ramani, Ronish et al.
Proceedings of 2023 IEEE Technology and Engineering Management Conference - Asia Pacific, TEMSCON-ASPAC 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (Proceedings of 2023 IEEE Technology and Engineering Management Conference - Asia Pacific, TEMSCON-ASPAC 2023).

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

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T2 - 2023 IEEE Technology and Engineering Management Conference - Asia Pacific, TEMSCON-ASPAC 2023

AU - Talaviya, Jeel

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AU - Mahadeva, Rajesh

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AB - Heart disease remains one of the leading causes of death worldwide, underscoring the critical need for accurate and timely prediction models. The outcome of cardiac disease is one area where machine learning algorithms have shown substantial potential. A rapidly advancing area of research is focused on using machine learning for heart disease prediction. Recent studies have extensively explored machine learning methods to anticipate heart disease in patients. This research aims to develop precise prediction models that can identify individuals at high risk of developing heart disease. These models consider various characteristics such as age, gender, medical history, and lifestyle choices to calculate the likelihood of heart disease. Notably, the accuracy of these machine learning models often surpasses that of traditional methods used for predicting cardiac disease. Integrating machine learning algorithms into heart disease diagnosis and treatment can improve patient outcomes and overall health.

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KW - Machine Learning

KW - Predictive Modeling

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T3 - Proceedings of 2023 IEEE Technology and Engineering Management Conference - Asia Pacific, TEMSCON-ASPAC 2023

BT - Proceedings of 2023 IEEE Technology and Engineering Management Conference - Asia Pacific, TEMSCON-ASPAC 2023

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ER -

Talaviya J, Trivedi D, Ramani R, Diwan A, Mahadeva R, Mahadeva R. A Data-Driven Approach to Cardiac Health: Machine Learning for Heart Disease Prediction. In Proceedings of 2023 IEEE Technology and Engineering Management Conference - Asia Pacific, TEMSCON-ASPAC 2023. Institute of Electrical and Electronics Engineers Inc. 2023. (Proceedings of 2023 IEEE Technology and Engineering Management Conference - Asia Pacific, TEMSCON-ASPAC 2023). doi: 10.1109/TEMSCON-ASPAC59527.2023.10531380

A Data-Driven Approach to Cardiac Health: Machine Learning for Heart Disease Prediction

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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