Cardio Vascular Disease (CVD) Risk Prediction using Supervised Learning

Author(s)	Vartika Trivedi, Ghanshyam Chaurasia, Abhishek Kumar Saxena
Country	India
Abstract	Our main goal is to develop a cardiovascular disease (CVD) risk prediction model using supervised learning classifiers that can be used in expert decision with maximum accuracy whether heart disease is present or not. It will prove to be very important to medicine for the diagnosis of heart diseases such as heart attack, heart failure, stroke and other cardiovascular diseases. If such predictions give good results with sufficient accuracy, we can not only avoid inaccurate diagnoses, but also save unnecessary resources. When a patient who does not have heart disease is diagnosed positively, he panics unnecessarily, and when a patient who does have heart disease and is neither diagnosed with heart disease nor has a negative result, he dies will involuntarily miss a chance to cure his illness. Such misdiagnosis is detrimental to both patients and hospitals. With more accurate predictions, we can overcome unnecessary problems.
Keywords	CVD, ML, Supervised Learning
Field	Engineering
Published In	Volume 5, Issue 4, July-August 2023
Published On	2023-07-04
Cite This	Cardio Vascular Disease (CVD) Risk Prediction using Supervised Learning - Vartika Trivedi, Ghanshyam Chaurasia, Abhishek Kumar Saxena - IJFMR Volume 5, Issue 4, July-August 2023. DOI 10.36948/ijfmr.2023.v05i04.4183
DOI	https://doi.org/10.36948/ijfmr.2023.v05i04.4183
Short DOI	https://doi.org/gsfxpk

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