Machine Learning Approach for Early Detection of Heart Disease

Santhosh Moolya; N. Gopalakrishna Kini

doi:10.1007/978-981-96-3420-0_7

Machine Learning Approach for Early Detection of Heart Disease

Santhosh Moolya
, N. Gopalakrishna Kini^*

^*Corresponding author for this work

School of Computer Engineering

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

Abstract

In recent years, Cardiovascular diseases (CVDs) have been a major health issue. Due to CVDs, around 17.9 million deaths are occurring annually. Therefore, early detection of the disease and treating it is very necessary. This paper addresses the need for efficient early diagnosis and intervention to reduce the burden of heart disease. The paper emphasizes the ability of machine learning in predicting heart disease development based on identifiable risk factors. The study shows the transformative impact of machine learning algorithms in heart disease prediction, which improves healthcare outcomes and reduces heart disease incidents. In this paper, the machine learning model is trained using several widely used and most popular classification algorithms. The models are evaluated using different evaluation metrics. Random Forest gave the best result with an accuracy of 91.80%, thus outperforming other models.

Original language	English
Title of host publication	Human-Centric Smart Computing - Proceedings of ICHCSC 2024
Editors	Siddhartha Bhattacharyya, Jan Platos, Siddhartha Bhattacharyya, Jyoti Sekhar Banerjee, Mario Köppen, Somen Nayak
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	75-85
Number of pages	11
ISBN (Print)	9789819634194
DOIs	https://doi.org/10.1007/978-981-96-3420-0_7
Publication status	Published - 2025
Event	3rd International Conference on Human-Centric Smart Computing, ICHCSC 2024 - Jaipur, India Duration: 25-07-2024 → 26-07-2024

Publication series

Name	Smart Innovation, Systems and Technologies
Volume	440 SIST
ISSN (Print)	2190-3018
ISSN (Electronic)	2190-3026

Conference

Conference	3rd International Conference on Human-Centric Smart Computing, ICHCSC 2024
Country/Territory	India
City	Jaipur
Period	25-07-24 → 26-07-24

UN SDGs

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

SDG 3 Good Health and Well-being

All Science Journal Classification (ASJC) codes

General Decision Sciences
General Computer Science

Access to Document

10.1007/978-981-96-3420-0_7

Cite this

Moolya, S., & Gopalakrishna Kini, N. (2025). Machine Learning Approach for Early Detection of Heart Disease. In S. Bhattacharyya, J. Platos, S. Bhattacharyya, J. S. Banerjee, M. Köppen, & S. Nayak (Eds.), Human-Centric Smart Computing - Proceedings of ICHCSC 2024 (pp. 75-85). (Smart Innovation, Systems and Technologies; Vol. 440 SIST). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-96-3420-0_7

Moolya, Santhosh ; Gopalakrishna Kini, N. / Machine Learning Approach for Early Detection of Heart Disease. Human-Centric Smart Computing - Proceedings of ICHCSC 2024. editor / Siddhartha Bhattacharyya ; Jan Platos ; Siddhartha Bhattacharyya ; Jyoti Sekhar Banerjee ; Mario Köppen ; Somen Nayak. Springer Science and Business Media Deutschland GmbH, 2025. pp. 75-85 (Smart Innovation, Systems and Technologies).

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title = "Machine Learning Approach for Early Detection of Heart Disease",

abstract = "In recent years, Cardiovascular diseases (CVDs) have been a major health issue. Due to CVDs, around 17.9 million deaths are occurring annually. Therefore, early detection of the disease and treating it is very necessary. This paper addresses the need for efficient early diagnosis and intervention to reduce the burden of heart disease. The paper emphasizes the ability of machine learning in predicting heart disease development based on identifiable risk factors. The study shows the transformative impact of machine learning algorithms in heart disease prediction, which improves healthcare outcomes and reduces heart disease incidents. In this paper, the machine learning model is trained using several widely used and most popular classification algorithms. The models are evaluated using different evaluation metrics. Random Forest gave the best result with an accuracy of 91.80\%, thus outperforming other models.",

author = "Santhosh Moolya and \{Gopalakrishna Kini\}, N.",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.; 3rd International Conference on Human-Centric Smart Computing, ICHCSC 2024 ; Conference date: 25-07-2024 Through 26-07-2024",

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doi = "10.1007/978-981-96-3420-0\_7",

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isbn = "9789819634194",

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pages = "75--85",

editor = "Siddhartha Bhattacharyya and Jan Platos and Siddhartha Bhattacharyya and Banerjee, \{Jyoti Sekhar\} and Mario K{\"o}ppen and Somen Nayak",

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Moolya, S & Gopalakrishna Kini, N 2025, Machine Learning Approach for Early Detection of Heart Disease. in S Bhattacharyya, J Platos, S Bhattacharyya, JS Banerjee, M Köppen & S Nayak (eds), Human-Centric Smart Computing - Proceedings of ICHCSC 2024. Smart Innovation, Systems and Technologies, vol. 440 SIST, Springer Science and Business Media Deutschland GmbH, pp. 75-85, 3rd International Conference on Human-Centric Smart Computing, ICHCSC 2024, Jaipur, India, 25-07-24. https://doi.org/10.1007/978-981-96-3420-0_7

Machine Learning Approach for Early Detection of Heart Disease. / Moolya, Santhosh; Gopalakrishna Kini, N.
Human-Centric Smart Computing - Proceedings of ICHCSC 2024. ed. / Siddhartha Bhattacharyya; Jan Platos; Siddhartha Bhattacharyya; Jyoti Sekhar Banerjee; Mario Köppen; Somen Nayak. Springer Science and Business Media Deutschland GmbH, 2025. p. 75-85 (Smart Innovation, Systems and Technologies; Vol. 440 SIST).

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

TY - GEN

T1 - Machine Learning Approach for Early Detection of Heart Disease

AU - Moolya, Santhosh

AU - Gopalakrishna Kini, N.

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

PY - 2025

Y1 - 2025

N2 - In recent years, Cardiovascular diseases (CVDs) have been a major health issue. Due to CVDs, around 17.9 million deaths are occurring annually. Therefore, early detection of the disease and treating it is very necessary. This paper addresses the need for efficient early diagnosis and intervention to reduce the burden of heart disease. The paper emphasizes the ability of machine learning in predicting heart disease development based on identifiable risk factors. The study shows the transformative impact of machine learning algorithms in heart disease prediction, which improves healthcare outcomes and reduces heart disease incidents. In this paper, the machine learning model is trained using several widely used and most popular classification algorithms. The models are evaluated using different evaluation metrics. Random Forest gave the best result with an accuracy of 91.80%, thus outperforming other models.

AB - In recent years, Cardiovascular diseases (CVDs) have been a major health issue. Due to CVDs, around 17.9 million deaths are occurring annually. Therefore, early detection of the disease and treating it is very necessary. This paper addresses the need for efficient early diagnosis and intervention to reduce the burden of heart disease. The paper emphasizes the ability of machine learning in predicting heart disease development based on identifiable risk factors. The study shows the transformative impact of machine learning algorithms in heart disease prediction, which improves healthcare outcomes and reduces heart disease incidents. In this paper, the machine learning model is trained using several widely used and most popular classification algorithms. The models are evaluated using different evaluation metrics. Random Forest gave the best result with an accuracy of 91.80%, thus outperforming other models.

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ER -

Moolya S, Gopalakrishna Kini N. Machine Learning Approach for Early Detection of Heart Disease. In Bhattacharyya S, Platos J, Bhattacharyya S, Banerjee JS, Köppen M, Nayak S, editors, Human-Centric Smart Computing - Proceedings of ICHCSC 2024. Springer Science and Business Media Deutschland GmbH. 2025. p. 75-85. (Smart Innovation, Systems and Technologies). doi: 10.1007/978-981-96-3420-0_7

Machine Learning Approach for Early Detection of Heart Disease

Abstract

Publication series

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UN SDGs

All Science Journal Classification (ASJC) codes

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