Effective Heart Disease Prediction using Machine Learning Algorithms
Published: 2023
Author(s) Name: Joban K. Joseph, Jahana Jabbar, Manual Soman, Nasweeba K. N. and Athira Manikuttan |
Author(s) Affiliation: SCMS School of Engineering & Technology, Karukutty, Ernakulam District, Kerala, India.
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Abstract
Cardiovascular diseases are a leading cause of death globally, resulting in 17.9 million deaths each year, according to a new report by the World Health Organization. However, with the advancement of technology, machine learning approaches have shown promise in the health industry, providing an opportunity to diagnose and treat heart disease at an earlier stage. In this research project, we aim to build a machine learning model to predict the likelihood of heart disease based on relevant factors. We use a Kaggle heart disease dataset, which includes a comprehensive list of factors related to heart disease, and employ various machine learning algorithms such as Naive Bayes, Support Vector Machine, Random Forest, K-NN, and Decision Tree. Our results indicate that Random Forest provides better prediction accuracy in less time than other machine learning approaches, making it an effective decision support system for medical professionals. This project has the potential to jnisimprove the diagnosis and treatment of heart disease and ultimately save lives.
Keywords: K-NN, Naïve Bayes, Random forest, Support vector machine
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