Proceedings of the 1st International Conference on Artificial Intelligence, Communication, IoT, Data Engineering and Security, IACIDS 2023, 23-25 November 2023, Lavasa, Pune, India

Research Article

A Novel Approach for Heart Disease prediction using Artificial Intelligence Techniques

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  • @INPROCEEDINGS{10.4108/eai.23-11-2023.2343179,
        author={Sathyavathy  V},
        title={A Novel Approach for Heart Disease prediction using  Artificial Intelligence Techniques},
        proceedings={Proceedings of the 1st International Conference on Artificial Intelligence, Communication, IoT, Data Engineering and Security, IACIDS 2023, 23-25 November 2023, Lavasa, Pune, India},
        publisher={EAI},
        proceedings_a={IACIDS},
        year={2024},
        month={3},
        keywords={cardiovascular disease random forest algorithm artificial intelligence logistic regression},
        doi={10.4108/eai.23-11-2023.2343179}
    }
    
  • Sathyavathy V
    Year: 2024
    A Novel Approach for Heart Disease prediction using Artificial Intelligence Techniques
    IACIDS
    EAI
    DOI: 10.4108/eai.23-11-2023.2343179
Sathyavathy V1,*
  • 1: KG College of Arts and Science
*Contact email: Sathyavathy.v@kgcas.com

Abstract

Despite major improvements in diagnosis and care, cardiovascular disease remains to be the world's major cause of morbidity and mortality. Due to the growth in bulk amount of data and complexity, Artificial Intelligence techniques like machine learning approaches and deep learning algorithms can help advance medical understanding by revealing data that is clinically useful. Many medical conditions can be identified, detected, and predicted using machine learning.This main goal of the study is to provide the best algorithmic implementation for early heart disease issue detection. As a result, it will be simpler to provide patients with the right care while minimising negative consequences. In this kind of condition, the heart often has trouble pumping enough blood to the rest of the body so that it can carry out its intended activities. The ability to diagnose this issue quickly and accurately is critical for losing patient’s lives and reducing further damage. On the other hand, antagonistic based approaches are suggested to be more accurate and dependable for the detection of heart illness, such as computational algorithms based on intellectual learning. For identifying and diagnosing heart illness, an intelligent computational predictive system is presented in this article.