Machine Learning Techniques for Cardiac Risk Analysis

One of the most difficult tasks in medicine is predicting cardiac disease. Heart disease is becoming more common at an alarming rate, and being able to predict such diseases in advance is crucial and important. Because this is a difficult diagnosis to do, it must be completed correctly and swiftly....

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Bibliographic Details
Published in:	2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS) pp. 91 - 95
Main Authors:	Medasani, Hari Kumar, Kottamasu, Sai Anila, Doppalapudi, Sriram, Koneru, Suvarnavani
Format:	Conference Proceeding
Language:	English
Published:	IEEE 07-04-2022
Subjects:	Cardiac disease Decision Tree Health Metrics Heart Heart Disease Prediction Logistic Regression Machine Learning Machine learning algorithms Prediction algorithms Random Forest Random forests Regression tree analysis Task analysis
Online Access:	Get full text
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Summary:	One of the most difficult tasks in medicine is predicting cardiac disease. Heart disease is becoming more common at an alarming rate, and being able to predict such diseases in advance is crucial and important. Because this is a difficult diagnosis to do, it must be completed correctly and swiftly. We developed a machine learning technique to predict whether or not a patient will be diagnosed with heart disease based on their medical history. Physical characteristics, clinical laboratory test results and Symptoms can be examined and used for predicting cardiac disease in electronic medicalrecords. For pre dic ting car di ac related dis eas e, algorithms such as Random Forest, Decision Tree, Logistic Regression were used Among these, logistic regression produces the most accurate results.
DOI:	10.1109/ICSCDS53736.2022.9760743