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Salud, Ciencia y Tecnología Metaverse Basic and Applied Research Data & Metadata Interdisciplinary Rehabilitation Salud, Ciencia y Tecnología - Serie de Conferencias Community and Interculturality in Dialogue Seminars in Medical Writing and Education Health Leadership and Quality of Life Gamification and Augmented Reality Southern perspective / Perspectiva austral
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Published: 2024-03-23
DOI: https://doi.org/10.56294/sctconf2024653

Design of a Classifier model for Heart Disease Prediction using normalized graph model

By
B. Karthiga ,
B. Karthiga

Dhanalakshmi Srinivasan Engineering College, Perambalur, India

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Sathya Selvaraj Sinnasamy ,
Sathya Selvaraj Sinnasamy

SRM Institute of Science and Technology, Chennai, India

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V.C. Bharathi ,
V.C. Bharathi

School of Computer Science and Engineering, VIT-AP University, Amaravathi, Andhra Pradesh, India

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Azarudeen ,
Azarudeen

Department of Computer Science and Engineering, Velammal College of Engineering and Technology, Madurai, India

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Sherubha. P ,
Sherubha. P

Department of Information Technology, Karpagam College of Engineering, Coimbatore, India

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Abstract

Heart disease is an illness that influences enormous people worldwide. Particularly in cardiology, heart disease diagnosis and treatment need to happen quickly and precisely. Here, a machine learning-based (ML) approach is anticipated for diagnosing a cardiac disease that is both effective and accurate. The system was developed using standard feature selection algorithms for removing unnecessary and redundant features. Here, a novel normalized graph model (n-GM) is used for prediction. To address the issue of feature selection, this work considers the significant information feature selection approach. To improve classification accuracy and shorten the time it takes to process classifications, feature selection techniques are utilized. Furthermore, the hyper-parameters and learning techniques for model evaluation have been accomplished using cross-validation. The performance is evaluated with various metrics. The performance is evaluated on the features chosen via features representation. The outcomes demonstrate that the suggested n-GM gives 98% accuracy for modeling an intelligent system to detect heart disease using a classifier support vector machine

Keywords:

Heart disease,
classification,
prediction,
machine learning,
hyper-parameters,

How to Cite

1.
Karthiga B, Selvaraj Sinnasamy S, Bharathi V, Azarudeen K, Sherubha P. Design of a Classifier model for Heart Disease Prediction using normalized graph model. Salud, Ciencia y Tecnología - Serie de Conferencias [Internet]. 2024 Mar. 23 [cited 2024 Jun. 26];3:653. Available from: https://conferencias.saludcyt.ar/index.php/sctconf/article/view/653

Copyright Notice

The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.

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Vol. 3 (2024)
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