A Machine Learning Based Artificial Intelligence Model for Detecting Heart Illness

Year : 2024 | Volume :15 | Issue : 01 | Page : 50-58
By

    Aditya Singh Chauhan

  1. Riya Kushwah

  2. Praveen Kumar Rawat

  3. Anshul Chandra

  4. Ghanshyam Prasad Dubey

  1. Student, Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University, Gwalior, Madhya Pradesh, India
  2. Student, Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University, Gwalior, Madhya Pradesh, India
  3. Student, Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University, Gwalior, Madhya Pradesh, India
  4. Student, Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University, Gwalior, Madhya Pradesh, India
  5. Associate Professor, Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University, Gwalior, Madhya Pradesh, India

Abstract

This study centers around the improvement of an artificial intelligence- and computerized reasoning-based heart sickness determination framework. We exhibit how AI can help with foreseeing whether an individual will get cardiovascular infection. In this review, a Python-based application for medical care research is created since it is more reliable and helps track and lay out many kinds of well-being observing applications. We show information handling, which incorporates working with all out factors and changing over unmitigated sections. This paper covers the three significant phases of utilization improvement: gathering information bases, applying calculated relapse, and evaluating the dataset’s properties. A random forest order framework is being created to more readily analyze heart issues. This application, which is highly reliable in light of the fact that it has around 83% precision rate across preparing information, requires information examination. The random forest classifier method is next examined, including the preliminaries and discoveries, which give further developed correctness to investigate determination. The paper finishes up with targets, constraints, and exploration commitments.

Keywords: Artificial intelligence, heart disease detection system, machine learning, predictive analytics, random forest classifier algorithm

[This article belongs to Journal of Computer Technology & Applications(jocta)]

How to cite this article: Aditya Singh Chauhan, Riya Kushwah, Praveen Kumar Rawat, Anshul Chandra, Ghanshyam Prasad Dubey.A Machine Learning Based Artificial Intelligence Model for Detecting Heart Illness.Journal of Computer Technology & Applications.2024; 15(01):50-58.
How to cite this URL: Aditya Singh Chauhan, Riya Kushwah, Praveen Kumar Rawat, Anshul Chandra, Ghanshyam Prasad Dubey , A Machine Learning Based Artificial Intelligence Model for Detecting Heart Illness jocta 2024 {cited 2024 Apr 18};15:50-58. Available from: https://journals.stmjournals.com/jocta/article=2024/view=143652


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Regular Issue Subscription Review Article
Volume 15
Issue 01
Received February 28, 2024
Accepted April 8, 2024
Published April 18, 2024
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