Please use this identifier to cite or link to this item:
http://ir.juit.ac.in:8080/jspui/jspui/handle/123456789/9839
Title: | Cardiovascular Diseases (CVD) Prediction Models: a systematic review |
Authors: | Luthra, Raghav Bisht, Gopal Singh [Guided by] Sharma, Vipul Kumar [Guided by] |
Keywords: | Cardiovascular diseases Artificial intelligence |
Issue Date: | 2023 |
Publisher: | Jaypee University of Information Technology, Solan, H.P. |
Abstract: | Cardiovascular diseases (CVD) have the highest mortality rate in the Indian healthcare system. The aim of this study and report is to collect medical data related to cardiovascular diseases and extract the maximum features and implement them in machine learning based algorithms to predict CVD risk. This systematic review is rich in data visualisation and model implementation along with an exhaustive analysis of performance metrics especially sensitivity and specificity analysis such as accuracy, precision and recall. The usefulness and utility of the best model that can accurately capture data and effectively predict the risk of CVD is worked on in this report. |
Description: | Enrolment No. 191903 |
URI: | http://ir.juit.ac.in:8080/jspui/jspui/handle/123456789/9839 |
Appears in Collections: | B.Tech. Project Reports |
Files in This Item:
File | Description | Size | Format | |
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Cardiovascular diseases (CVD) prediction models a systematic review.pdf | 1.12 MB | Adobe PDF | View/Open |
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