Predicting Heart Disease Risk Using an Ensemble AdaBoost Supervised Machine Learning Classifier

Authors

  • Hardik J. Prajapati, Dushyantsinh B. Rathod

Keywords:

supervised, confusion matrix, linear regression, unsupervised, python, reinforced

Abstract

The heart is a crucial component of all living beings. The diagnosis and prognosis of heart illness need enhanced completeness and accuracy, since even a little error may result in severe complications or loss of life; heart-related fatalities are many and increasing quickly each day. A system that can forecast the spread of diseases is essential for finding a solution to this issue. An example of artificial intelligence is machine learning. When it comes to predicting the outcomes of all kinds of natural catastrophes, it offers exceptional help. Using the UCI benchmark data sets for training and testing, we determine the accuracy of four machine learning algorithms—k-proximal neighbours, Naïve Bayes, voting classifier, and ADABOOST—in predicting the occurrence of heart disease. Because it comes with a wide variety of libraries and header files, the Anaconda (Jupyter) notebook is the greatest tool for implementing Python programming.

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Published

26.03.2024

How to Cite

Hardik J. Prajapati. (2024). Predicting Heart Disease Risk Using an Ensemble AdaBoost Supervised Machine Learning Classifier. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 4772 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/7027

Issue

Vol. 12 No. 21s (2024)

Section

Research Article

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