Prediction of Coronary Heart Disease using Supervised Machine Learning Algorithms

2019 ◽  
Author(s):  
Divya Krishnani ◽  
Anjali Kumari ◽  
Akash Dewangan ◽  
Aditya Singh ◽  
Nenavath Srinivas Naik
Keyword(s):  
Machine Learning ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning

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

scholarly journals Heart Disease Prediction using Supervised Machine Learning Algorithms

2020 ◽  
Vol 9 (4) ◽  
pp. 148-151
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Classification Algorithms ◽  
Disease Detection ◽  
Disease Prediction ◽  
Benchmark Datasets ◽  
Complicated Task

Generally, the most complicated task in the healthcare field is the diagnosis of the disease itself. The diagnosis phase in disease detection is usually the most time-consuming task and is prone to most of the errors. Such complications can be effectively handled if the disease detection process is well automated by incorporating effective machine learning algorithms trained with some benchmark datasets. It should also be noted that huge amounts of data that are acquired from Heart Specialization Hospitals are being wasted every year. In this paper, various classification algorithms have been used to train the machine to diagnose heart disease. By a comparative study of various learning models, we have identified the appropriate learning model for the heart disease dataset. Initially, the work will begin with an overview of various machine learning algorithms followed by the algorithmic comparison.

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