Abstract
In the present pandemic situation, health care data is generated voluminously in an unstructured format posing challenge to technology in perspective of analysis, classification and prediction. The data generated is converted to structured format. Suitability of methodology keeping in mind low computational complexity and high accuracy is a major concern which has emerged as a problem in data science. In this research work real time heart disease data set is considered to evaluate the accuracy of six supervised methods –SVM (Support Vector Machine), KNN (K-Nearest Neighbor), GNB (Gaussian Naïve Bayes), LR (Logistic Regression), DT (Decision Tree) and RF (Random Forest). Analysis through ROC curve and confusion matrix predominantly justify RF classifier and LR gives efficient results compared to other methods. This is a preprocessing stage; every researcher has to perform before deciding the methodology to be considered for further processing.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Innovative Technology and Exploring Engineering
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
