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.
Highlights
In underdeveloped, emerging and even developed countries, heart illness is the greatest cause of demise, lakhs of people die every year because of this
In order to reliably envisage the existence of heart illness for a specific sufferer, this paper [1] analyzes different ensemble approaches Bagged Tree, Random Forest, and AdaBoost with the variable sub categorization process - Particle Swarm Optimization (PSO)
The most popular ensemble learning algorithms, Random Forest and Support Vector Machine were implemented in this system [8] to construct a classifier model that will predict disease with greater performance and accuracy
Summary
In underdeveloped, emerging and even developed countries, heart illness is the greatest cause of demise, lakhs of people die every year because of this. Random Forest and AdaBoost ensemble Machine Learning Procedures are used to predict heart disease. On data samples, the random forest algorithm produces decision trees and gets the prediction from each of them and selects the best solution by voting. It uses a variety of decision trees and predicts the more correct outcome in the case of regression and voting in the case of classification by averaging. AdaBoost uses decision stamps but any machine learning algorithm if it accepts weight on the training data set can be used. For both grouping and deterioration complications, AdaBoost algorithms can be used
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