Comparative Analysis of Heart Disease Prediction System

Year : 2023 | Volume :01 | Issue : 01 | Page : 1-6
By
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Kaveti Yeshwanth,

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Deepika Ghai,

  1. Student, School of Electronics and Electrical Engineering Lovely, Professional University, Jalandhar, Punjab, India
  2. Professor, School of Electronics and Electrical Engineering, Lovely Professional University Jalandhar, Punjab, India

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In the present world, where heart illnesses are on the rise, it is crucial to forecast these diseases. Performing the task on heart disease is a bit difficult and it must be finished precisely and successfully. Heart disease identification relies heavily on Machine Learning (ML) and data mining approaches. The primary focus of the review paper is that patients are easily prone to cardiac diseases depending on medical traits. Using the patient’s medical history, we compared techniques to anticipate in case the patient may be cured of heart disease or not. In this text, the editorial has practiced various types of machine learning algorithms such as Random Forest (RF), Logistic Regression (LR), Decision Trees (DT), K-Nearest Neighbor (KNN), Naive Bayes (NB), Support Vector Machine (SVM), etc. It has been observed that K- Nearest Neighbor along with Random Forest is the best among all heart disease prediction systems.

Keywords: Heart disease, Machine Learning, Data Mining, Cardiac condition, K-Nearest Neighbor, Logistic regression, Support Vector Machine, Random Forest Classifier

[This article belongs to International Journal of Advance in Molecular Engineering (ijame)]

How to cite this article:
Kaveti Yeshwanth, Deepika Ghai. Comparative Analysis of Heart Disease Prediction System. International Journal of Advance in Molecular Engineering. 2023; 01(01):1-6.
How to cite this URL:
Kaveti Yeshwanth, Deepika Ghai. Comparative Analysis of Heart Disease Prediction System. International Journal of Advance in Molecular Engineering. 2023; 01(01):1-6. Available from: https://journals.stmjournals.com/ijame/article=2023/view=0

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Regular Issue Subscription Review Article
Volume 01
Issue 01
Received 21/07/2023
Accepted 10/08/2023
Published 12/10/2023
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