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CardioRisk Pro (Heart Disease Prediction System)

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dc.contributor.author Gunathilaka, Sayuru
dc.date.accessioned 2024-04-04T04:01:40Z
dc.date.available 2024-04-04T04:01:40Z
dc.date.issued 2023
dc.identifier.citation Gunathilaka, Sayuru (2023) CardioRisk Pro (Heart Disease Prediction System). BSc. Dissertation, Informatics Institute of Technology en_US
dc.identifier.issn 20191061
dc.identifier.uri http://dlib.iit.ac.lk/xmlui/handle/123456789/1978
dc.description.abstract "Heart disease is a prevalent and severe health condition that impacts a vast number of people globally. Being one of the top causes of mortality, timely detection and accurate prediction of heart disease can play a vital role in enhancing patient outcomes. To achieve this, researchers have turned to advanced machine learning and deep learning techniques for developing predictive models. The heart disease prediction system proposed here leverages various machine learning algorithms and deep learning techniques, such as decision trees, neural networks, random forests, and support vector machines (SVM), to estimate the likelihood of heart disease occurrence. The proposed heart disease prediction system has the potential to revolutionize healthcare by diagnosing heart disease. Results show that the proposed system achieves high accuracy in predicting heart disease, and outperforms existing prediction models. Ultimately, the proposed system can help reduce healthcare costs and improve patient outcomes." en_US
dc.language.iso en en_US
dc.subject Heart Disease Prediction en_US
dc.subject Machine Learning en_US
dc.subject Ensemble Learning en_US
dc.title CardioRisk Pro (Heart Disease Prediction System) en_US
dc.type Thesis en_US


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