A Survey on Cardiovascular Prediction using Variant Machine learning

¹ PG Student, Computer Science and Engineering, GRIET, Hyderabad, Telangana, India.
² Professor, Computer Science and Engineering, GRIET, Hyderabad, Telangana, India.
³ Assistant Professor, Computer Science and Engineering, GRIET, Hyderabad, Telangana, India.
⁴ Assistant Professor, Computer Science and Engineering, GRIET, Hyderabad, Telangana, India.

^* Corresponding author: lingalachandrika97@gmail.com

Abstract

Prediction of a cardiovascular diseases has always a tedious challenge for doctors and medical practitioners. Most of the practitioners and hospital staff offers expensive medication, care and surgeries to treat the cardiovascular patients. At early-stage of prediction of heart-oriented problems will be giving a chance of survival by taking necessary precautions. Over the years there are different types of methodologies were proposed to predict the cardiovascular diseases one of the best methodologies is a Machine learning approach. These years many scientific advancements take place in the Artificial Intelligence, Machine learning, and Deep learning which gives an extra push up to help and implement the path in the field of medical image processing and medical data analysis. By using the enormous dataset from various medical experts used to help the researchers to predict the coronary problems prior to happening. Many researchers have tried and implemented different machine learning algorithms to automate the prediction analysis using the enormous number of datasets. There are numerous algorithms and procedures to predict the cardiovascular diseases and accessible to be specific Classification methods including Artificial Neural Networks (AI), Decision tree (DT), Support vector machine (SVM), Genetic algorithm (GA), Neural network (NN), Naive Bayes (NB) and Clustering algorithms like K-NN. A few examinations have been done for creating expectation models utilizing singular procedures and additionally concatenating at least two strategies. This paper gives a speedy and simple survey and knowledge of approachable prediction models using different researchers work from 2004 to 2019. The examination indicates the precision of individual experiments done by various researchers.