Please use this identifier to cite or link to this item: http://ir.juit.ac.in:8080/jspui/jspui/handle/123456789/8317
Title: Taxonomy on Machine Learning Based Techniques to Identify the Heart Disease
Authors: Srivastava, Anand Kumar
Singh, Pradeep Kumar
Kumar, Yugal
Keywords: Heart disease
Data mining
Machine learning
Issue Date: 2019
Publisher: Springer Nature Singapore Pte Ltd.
Abstract: Every year average death of human being is 17.7 million caused by Heart Disease or Cardiovascular diseases (CVDs), which is 31% of all global deaths reflected in Survey of World Heart Day 2017 [33]. In September 2016, many countries have taken the various Global Hearts Initiative in prediction and diagnosis of heart Diseases at earlier stages so that it can be cure perfectly [5]. Many authors have studied in this filed to optimize the performance of various ML techniques using various approaches. In latest studies, many groups have uncovered that many optimization algorithm like Differential Evolution, Genetic Variants, and Particle Swarm optimization are associated with prediction algorithm like K-Nearest neighbor, Decision Tree, Neural Network, Support Vector machine, Logistic Regression etc. to make efficient medical system for CVDs. The Objective of our current study is to analyze the comparatively study of the ML techniques in terms of performance measure of different ML techniques that have been used by various authors in their research work in earlier studies of heart disease prediction and diagnosis process.
URI: http://ir.juit.ac.in:8080/jspui/jspui/handle/123456789/8317
Appears in Collections:Book Chapters

Files in This Item:
File Description SizeFormat 
A Taxonomy on Machine Larning Based techniquees to identify the Heart Disease.pdf840.92 kBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.