Predicting and Prohibiting the Risk of Heart Failure Using Machine Learning

Year : 2023 | Volume : 01 | Issue : 01 | Page : 15-20
By

    Sumit Mali

  1. Aashish Gaike

  1. Assistant Professor, Department of Computer sciences and Engineering, NBN Sinhgad School of Engineering, Pune,, Maharashtra, India
  2. Student, Department of Computer sciences and Engineering, NBN Sinhgad School of Engineering, Pune,, Maharashtra, India

Abstract

It is challenging to estimate the likelihood of complex chronic disease while treating conditions like heart failure. The application of machine learning, an area of artificial intelligence, in cardiovascular care is growing quickly. In essence, it defines how computers classify and understand data, or choose a task with or without human intervention. The theoretical underpinnings of machine learning are models that accept input data (such as images or text) and forecast results using a combination of mathematical optimization and statistical analysis (e.g., favorable, unfavorable, or neutral). Aiming to deliver results with an accuracy of 97.5%, the Heart Failure Prediction Model is based on machine learning algorithms like Random Forest and Logistic Regression. It includes sections that list the symptoms of cardiovascular diseases, offer preventative measures, and allow users to schedule doctor appointments. In order to create a prediction model based on machine learning, this document tries to capture the system needs and features. By using that, we can estimate the risk of heart disease. Heart disease symptoms can be reduced and understood with the help of machine learning. Additionally, the website is a subscription-based model with a unique attribute that includes several courses at various price points that are offered on the website related to their everyday activities. It consists of a yoga instructor and a nutritionist who will advise the customer on correct diet plans and exercise regimens.

Keywords: ML, machine learning, AI, artificial intelligence, random forest, logistic regression, heart failure prediction models

[This article belongs to International Journal of Computer Science Languages(ijcsl)]

How to cite this article: Sumit Mali, Aashish Gaike Predicting and Prohibiting the Risk of Heart Failure Using Machine Learning ijcsl 2023; 01:15-20
How to cite this URL: Sumit Mali, Aashish Gaike Predicting and Prohibiting the Risk of Heart Failure Using Machine Learning ijcsl 2023 {cited 2023 Jul 06};01:15-20. Available from: https://journals.stmjournals.com/ijcsl/article=2023/view=112125

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Regular Issue Subscription Review Article
Volume 01
Issue 01
Received June 7, 2023
Accepted June 20, 2023
Published July 6, 2023