A Machine Learning Based Artificial Intelligence  Model for Detecting Heart Illness

Author: Aditya Singh Chauhan, Riya Kushwah, Praveen Kumar Rawat, Anshul Chandra, Ghanshyam Prasad Dubey Journal of Computer Technology & Applications-STM Journals Issn: 2347-7229 Date: 2024-06-25 03:28 Volume: 15 Issue: 01 Keyworde: Artificial intelligence, heart disease detection system, machine learning, predictive analytics, random forest classifier algorithm Full Text PDF Submit Manuscript Journals


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. 

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

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