1 10 2 https://digitalresearch.bsu.edu/studentsymposium2021/files/original/ebd6aa70f6b37bcbf4062cedb9300d89.pdf 0a8480a010f0be42a40ec04cf5cd692e https://digitalresearch.bsu.edu/studentsymposium2021/files/original/504d1a7a38503d7eed26153456c34107.pdf a2cefe05fd1df28e32f4323c035d3995 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 A Comparison of Machine Learning Techniques in Predicting 10-year Risk of Coronary Heart Disease. Subject The topic of the resource Keywords: Coronary Heart disease, Model building, 10-year risk prediction of heart disease, Machine learning techniques. Description An account of the resource This study uses different supervised machine learning techniques to build models to predict the ten-year risk of coronary heart disease. It also compares the models to identify which has the highest prediction accuracy and the least test error rate. Creator An entity primarily responsible for making the resource Adeola Mary Olaniyan Contributor An entity responsible for making contributions to the resource No contributors Language A language of the resource English Type The nature or genre of the resource Heart study; Model Building, Risk prediction. Format The file format, physical medium, or dimensions of the resource PDF 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 A Comparison of Machine Learning Techniques in Predicting 10-year Risk of Coronary Heart Disease Subject The topic of the resource Keywords: 10-year risk prediction, Heart study, Model building Description An account of the resource This study uses different supervised machine learning techniques to build models to predict the 10-year risk of Coronary heart disease using the Framingham heart study dataset. It compares, identifies, and selects the model with the highest prediction accuracy and the lowest test error rate. Creator An entity primarily responsible for making the resource Adeola M. Olaniyan Contributor An entity responsible for making contributions to the resource No contributors Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Heart study: Model building, Risk prediction. https://digitalresearch.bsu.edu/studentsymposium2021/files/original/11784e7e5805c0af758745bfed21d74a.pdf 2831812508c053af871d738c69427f38 https://digitalresearch.bsu.edu/studentsymposium2021/files/original/17ea3af836d36b6380a86a56753f0575.pdf 0a8480a010f0be42a40ec04cf5cd692e 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 A Comparison of Machine Learning Techniques in Predicting 10-year Risk of Coronary Heart Disease. Subject The topic of the resource Keywords: Coronary Heart disease, Model building, 10-year risk prediction of heart disease, Machine learning techniques. Description An account of the resource This study uses different supervised machine learning techniques to build models to predict the ten-year risk of coronary heart disease. It also compares the models to identify which has the highest prediction accuracy and the least test error rate. Creator An entity primarily responsible for making the resource Adeola Mary Olaniyan Contributor An entity responsible for making contributions to the resource No contributors Language A language of the resource English Type The nature or genre of the resource Heart study; Model Building, Risk prediction. Format The file format, physical medium, or dimensions of the resource PDF 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 A Comparison of Machine Learning Techniques in Predicting 10-year Risk of Coronary Heart Disease. Subject The topic of the resource A Comparison of Machine Learning Techniques in Predicting 10-year Risk of Coronary Heart Disease. Description An account of the resource This study uses different supervised machine learning techniques to build models to predict the ten-year risk of coronary heart disease. It also compares the models to identify the model with the highest prediction accuracy and the least test error rate as the selected model. Creator An entity primarily responsible for making the resource Adeola M. Olaniyan Contributor An entity responsible for making contributions to the resource No contributors Language A language of the resource English Format The file format, physical medium, or dimensions of the resource PDF