Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Book Chapter Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource An efficient design and comparison of machine learning model for diagnosis of cardiovascular disease Subject The topic of the resource Bayes; Evaluation Metrics; Pre-process; Prediction; Supervised Description An account of the resource Cardiovascular disease has a significant global impact. Cardiovascular disease is the primary cause of disability and mortality in most developed countries. Cardiovascular disease is a condition that disturbs the structures and functionality of the heart and can also be called heart disease. Cardiovascular diseases require more precise, accurate, and reliable detection and forecasting because even a small inaccuracy might lead to fatigue or mortality. There are very few death occurrences related to cardio sickness, and the amount is expanding rapidly. Predicting this disease at its early stage can be done by employing Machine Learning (ML) algorithms, which may help reduce the number of deaths. Data pre-processing can be employed here to eliminate randomness in data, replace missing data, fill in default values if appropriate, and categorize features for forecasting and making decisions at various levels. This research investigates various parameters that are related to the cause of heart disease. Several models discussed here will come under the supervised learning type of algorithms like Support Vector Machine (SVM), K-nearest neighbor (KNN), and Nae Bayes (NB) algorithm. The existing dataset of heart disease patients from the Kaggle has been used for the analysis. The dataset includes 300 instances and 13 parameters and 1 label are used for prediction and testing the performance of various algorithms. Predicting the likelihood that a given patient will be affected by the cardiac disease is the goal of this research. The most important purpose of the study is to make better efficiency and precision for the detection of cardiovascular disease in which the target output ultimately matters whether or not a person has heart disease. 2023, Bentham Books imprint. All rights reserved. Creator An entity primarily responsible for making the resource Sahu D.N.; Sudhakar G.; Raju C.G.; Joshi H.; Kumbhkar M. Source A related resource from which the described resource is derived AI and IoT-based intelligent Health Care &amp; Sanitation, pp. 191-206. Publisher An entity responsible for making the resource available Bentham Science Publishers Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.2174/9789815136531123010015" target="_blank" rel="noreferrer noopener">https://doi.org/10.2174/9789815136531123010015</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205281009&amp;doi=10.2174%2F9789815136531123010015&amp;partnerID=40&amp;md5=afc84bc1b0735c9326e0bf66c4187d05" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205281009&amp;doi=10.2174%2f9789815136531123010015&amp;partnerID=40&amp;md5=afc84bc1b0735c9326e0bf66c4187d05</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-981513653-1; 978-981513654-8 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Book chapter Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Sahu D.N., Department of MCA, School of Computer Science, Gangadhar Meher University, Sambalpur, 768001, India; Sudhakar G., School of Information Technology, Jawaharlal Nehru Technological University, Hyderabad, 500085, India; Raju C.G., BMS College of Engineering, Bangalore, 560019, India; Joshi H., Department of Statistics, CHRIST (Deemed to be University), Bangalore, 560029, India; Kumbhkar M., Christian Eminent College, Indore, 452001, India