scholarly journals Heart Disease Detection Using Machine Learning

2020 ◽  
Author(s):  
Chithambaram T ◽  
Logesh Kannan N ◽  
Gowsalya M
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Human Body ◽  
Cardiac Disease ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Individual Data ◽  
Different Types ◽  
Python Programming ◽  
Target Output

Abstract This paper analyzes the detection of heart disease using machine learning algorithms and python programming. Over the post decades, heart disease is common and dangerous disease caused by fat containment. This disease occurs due to over pressure in the human body. Using different types of parameters in the dataset we can predict the cardiac-disease. We have observed a dataset consists of 12 parameters and 70000 individual data values[5] to analyze the performance of patients. The main objective of the paper is to get a better accuracy to detect the heart-disease using algorithms in which the target output counts that a person having heart disease or not.

scholarly journals Applying Ensemble Learning Algorithm in Early Prognosis of Heart Illness

2021 ◽  
Vol 12 (2) ◽  
pp. 1769-1774
Author(s):  
Lijetha.C. Jaffrin, Et. al.
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Heart Attacks ◽  
Ensemble Machine Learning ◽  
Ensemble Learning Algorithm ◽  
Cardiac Illness ◽  
Python Programming

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.

A Comparative Study of Two Machine Learning Algorithms for Heart Disease Prediction System

2021 ◽  
Author(s):  
Wan Adlina Husna Wan Azizan ◽  
A'zraa Afhzan Ab Rahim ◽  
Siti Lailatul Mohd Hassan ◽  
Ili Shairah Abdul Halim ◽  
Noor Ezan Abdullah
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Comparative Study ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Disease Prediction ◽  
Prediction System

scholarly journals Comparison of machine learning algorithms for clinical event prediction (risk of coronary heart disease)

2019 ◽  
Vol 97 ◽  
pp. 103257 ◽  
Author(s):  
Juan-Jose Beunza ◽  
Enrique Puertas ◽  
Ester García-Ovejero ◽  
Gema Villalba ◽  
Emilia Condes ◽  
...  
Keyword(s):  
Machine Learning ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Clinical Event ◽  
Event Prediction ◽  
Prediction Risk
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