Classification of Algorithms Supported Factual Knowledge Recovery from Cardiac Data Set

Abstract

Introduction: Improvised modern lifestyle with more fascination towards fast food causes severe anxieties over human health standards. This renders the society to visit the physicians often, which in turn generates terabytes of diagnostic data. The stored data on critical mining using algorithm provides a wealth of information to clinicians and back them to execute a better treatment. Heart disease rank’s first among the charted ailments due to its life-threatening concerns. Objectives: In the present work mining of cardiac data sets obtained from the University of California Irvine (UCI) repository was done using algorithms such as Linear Regression, Naive Bayes and Decision Stump algorithms in Waikato Environment for Knowledge Analysis (WEKA) environment. Result and Conclusion: The obtained results concluded that the Naive Bayes classifier offered the highest accuracy with specificity among the studied algorithms.