Predicting insulin dosage for diabetic patients to reach optimal glucose levels. (c2012)

LAUR Repository

Show simple item record

dc.contributor.author Bitar, Mandy
dc.date.accessioned 2012-05-31T08:15:25Z
dc.date.available 2012-05-31T08:15:25Z
dc.date.copyright 2012 en_US
dc.date.issued 2012-05-31
dc.date.submitted 2012-01-05
dc.identifier.uri http://hdl.handle.net/10725/1172
dc.description Includes bibliographical references (leaves 45-48). en_US
dc.description.abstract Endocrinologists treating diabetes mellitus find it overwhelming to keep track of the enormous amount of data gathered from the multiple measurements retrieved daily by their patients, throughout the years. This makes it hard for them to find a certain pattern in the data that could help them assign, to each patient, the optimal insulin dosage. This forces them to seek trial and error until they find the individualized insulin doses, required by each patient, to reach their optimal glucose levels. Hence, there is a great read to automate the process of estimating the glucose level. For this, we propose two machine learning techniques and one heuristic. In particular, we present C4.5, Case-Based Reasoning and genetic algorithms. We validate our approach on a data set obtained from the UCMI online machine learning repository. Obtained results are promising. Case-Based Reasoning outperformed both C4.5 and Genetic Algorithms. en_US
dc.language.iso en en_US
dc.subject Diabetes -- Treatment en_US
dc.subject Case-based reasoning en_US
dc.subject Artificial intelligence -- Medical applications en_US
dc.title Predicting insulin dosage for diabetic patients to reach optimal glucose levels. (c2012) en_US
dc.type Thesis en_US
dc.title.subtitle Machine learning approaches en_US
dc.term.submitted Fall en_US
dc.author.degree MS in Computer Science en_US
dc.author.school Arts and Sciences en_US
dc.author.idnumber 200500965 en_US
dc.author.commembers Dr. Haidar Harmanani
dc.author.commembers Dr. John Takchi
dc.author.woa OA en_US
dc.description.physdesc 1 bound copy: xii, 48 leaves; ill.; 31 cm. available at RNL. en_US
dc.author.division Computer Science en_US
dc.author.advisor Dr. Danielle Azar
dc.keywords Machine Learning en_US
dc.keywords Genetic Algorithms en_US
dc.keywords Case-Based Reasoning en_US
dc.keywords C4.5 en_US
dc.keywords Decision Trees en_US
dc.keywords Diabetes Mellitus en_US
dc.keywords Glucose level en_US
dc.keywords Insulin en_US
dc.identifier.doi https://doi.org/10.26756/th.2012.8 en_US
dc.publisher.institution Lebanese American University en_US

Files in this item

This item appears in the following Collection(s)

Show simple item record

Search LAUR

Advanced Search


My Account