Time Efficient IOS Application For CardioVascular Disease Prediction Using Machine Learning
Vansh Kedia, Swesh Raj Regmi, Khushi Jha, A. K. Bhatia, Siddhant Dugar, Bickey Kumar Shah
Abstract
This paper intends to utilize the vast amount of data generated in the healthcare industry by building machine learning models to predict the incidents of cardiovascular disease in people and hence allow them to take suitable preventive actions. The proposed research work has integrated these functionalities to build a mobile-based ios application using which a person enters details and views system prediction making it an efficient and easy to use interface for the people with time and accuracy. Making the system time efficient in the IOS is of greater importance in the paper. Cardiovascular disease is a class of heart-related disease involving blockage in blood vessels causing health problems like heart attack, chest pain, stroke, and possible heart failure. They are one of the biggest causes of morbidity and mor tality in the world and their incident is based on lifestyle hence making identification and prevention difficult. Future scope involves expanding the model to include an integrated prediction including another disease like diabetes and suggest feedback and health tips to the users for healthier lifestyle habits and preventive actions.