Cardiovascular Stroke Prediction System using Machine Learning Techniques

Over the past few decades, cardiovascular diseases have surpassed all other causes of death as the main killers in industrialised, underdeveloped, and developing nations. Early detection of heart conditions and clinical care can lower the death rate. Based on the patient's various cardiac featu...

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Bibliographic Details
Main Authors: K M, Anandkumar, R, Mohanakrishnamoorthy, F, Salman Faris, B, Mohan Raj
Format: Conference Proceeding
Language:English
Subjects:
Black Box
Cardiac arrest
Deep learning
Heart
Heart ailment
Logistic regression
Machine learning
Machine learning algorithms
Predictive models
Support vector machines
SVM
Online Access:Request full text
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Summary:Over the past few decades, cardiovascular diseases have surpassed all other causes of death as the main killers in industrialised, underdeveloped, and developing nations. Early detection of heart conditions and clinical care can lower the death rate. Based on the patient's various cardiac features, we proposed a model for forecasting heart disease and identifying impending heart disease using machine learning techniques such logistic regression, SVM, Multinomial Nave Bayes, Random Forest, and Decision Tree. In most cases, input is received through numerical data of various parameters, and output findings are generated in real-time, predicting whether or not the patient has a disease. We'll use a variety of supervised machine learning methods before deciding which one is best for the model. Existing systems rely on classical deep learning models, which are inefficient and imprecise. They aren't as accurate as the proposed model and take a little longer to process.
ISSN:2575-7288
DOI:10.1109/ICACCS57279.2023.10112727