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Using machine learning for disease detection. (c2013)

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dc.contributor.author Jreij, Georges Antoun
dc.date.accessioned 2016-03-04T09:48:39Z
dc.date.available 2016-03-04T09:48:39Z
dc.date.copyright 2013-06-11
dc.date.issued 2016-03-04
dc.identifier.uri http://hdl.handle.net/10725/3266
dc.description.abstract Classification consists of predicting group membership for new data instances by learning from pre-classified data instances. Classification is crucial as it contributes in solving problems in all fields, such as: bio-chemistry, social sciences, bioinformatics, etc. Classification has three main components: the classification algorithm, the pre-classified data (training data) and the un-classified data (testing data). Classification accuracy is a measure of how well a classification algorithm classifies the un-classified data. Several algorithms tackle this problem. Examples of such algorithms are C4.5, neural networks, Bayesian networks, etc. However, since algorithms do not perform equally on the same data, a detailed study of the “algorithm-data relationship” is needed to assess the overall performance of these algorithms rather than relying only on their accuracy. In order to rationalize this point of view, we will explore and assess eight classification algorithms on eight disease detection datasets with different characteristics each. A detailed comparative study will highlight the advantages and drawbacks of each algorithm. en_US
dc.language.iso en en_US
dc.subject Disease -- Classification en_US
dc.subject Lebanese American University -- Dissertations en_US
dc.subject Dissertations, Academic en_US
dc.title Using machine learning for disease detection. (c2013) en_US
dc.type Thesis en_US
dc.title.subtitle a comparative study en_US
dc.term.submitted Spring en_US
dc.author.degree MS in Computer Science en_US
dc.author.school SAS en_US
dc.author.idnumber 200402329 en_US
dc.author.commembers Takche, Jean
dc.author.commembers Khazen, George
dc.author.woa OA en_US
dc.author.department Computer Science and Mathematics en_US
dc.description.embargo N/A en_US
dc.description.physdesc 1 hard copy: xix, 146 leaves; ill.; 30 cm. available at RNL. en_US
dc.author.advisor Azar, Danielle
dc.keywords Classification via clustering en_US
dc.keywords Comparative study en_US
dc.keywords Decision trees en_US
dc.keywords Disease detection en_US
dc.keywords K nearest neighbor en_US
dc.keywords Logistic regression en_US
dc.keywords Machine learning en_US
dc.keywords Medical datasets en_US
dc.keywords Multilayered perceptron en_US
dc.keywords Naïve Bayes en_US
dc.keywords Neural networks en_US
dc.keywords Partial decision trees en_US
dc.keywords Voting feature intervals en_US
dc.description.bibliographiccitations Includes bibliographical references (leaves 138-146). en_US
dc.identifier.doi https://doi.org/10.26756/th.2013.49 en_US
dc.publisher.institution Lebanese American University en_US


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