Applying Ensemble Learning Algorithm in Early Prognosis of Heart Illness
Main Article Content
Abstract
Medical diagnosis and treatment of diseases are the key elements of machine learning algorithms nowadays. To find similarities between various diseases, machine learning algorithms are used. Many people are now dying due to sudden heart attacks. Predicting and diagnosing heart disease is a daunting aspect faced by physicians and hospitals around the world. There is a need to foreknow whether or not a person is at risk of heart syndrome in advance, in order to minimize the number of deaths due to heart disease. In this field, machine learning algorithms play a very significant role. Many researchers are carrying out their research in this field to create software that can assist doctors to make decisions about cardiac illness prognosis. In this paper, Random Forest and AdaBoost ensemble Machine Learning Procedures are used in advance to predict heart disease. The datasets are handled in python programming by means of Anaconda Spyder IDE to validate the machine learning algorithm.
Downloads
Metrics
Article Details
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.