{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/determining-the-best-classifier-for","title":"Determining the best classifier for predicting the value of a boolean field on a blood donor database using genetic algorithms","arxiv_id":"1802.07756","date":"2018-02-21","proceeding":null,"authors":["Ritabrata Maiti"],"abstract":"Motivation: Thanks to digitization, we often have access to large databases,\nconsisting of various fields of information, ranging from numbers to texts and\neven boolean values. Such databases lend themselves especially well to machine\nlearning, classification and big data analysis tasks. We are able to train\nclassifiers, using already existing data and use them for predicting the values\nof a certain field, given that we have information regarding the other fields.\nMost specifically, in this study, we look at the Electronic Health Records\n(EHRs) that are compiled by hospitals. These EHRs are convenient means of\naccessing data of individual patients, but there processing as a whole still\nremains a task. However, EHRs that are composed of coherent, well-tabulated\nstructures lend themselves quite well to the application to machine language,\nvia the usage of classifiers. In this study, we look at a Blood Transfusion\nService Center Data Set (Data taken from the Blood Transfusion Service Center\nin Hsin-Chu City in Taiwan). We used scikit-learn machine learning in python.\nFrom Support Vector Machines(SVM), we use Support Vector Classification(SVC),\nfrom the linear model we import Perceptron. We also used the\nK.neighborsclassifier and the decision tree classifiers. Furthermore, we use\nthe TPOT library to find an optimized pipeline using genetic algorithms. Using\nthe above classifiers, we score each one of them using k fold cross-validation.\n  Contact: ritabratamaiti@hiretrex.com GitHub Repository:\nhttps://github.com/ritabratamaiti/Blooddonorprediction","url_abs":"http://arxiv.org/abs/1802.07756v4","url_pdf":"http://arxiv.org/pdf/1802.07756v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"determining-the-best-classifier-for","repo_url":"https://github.com/ritabratamaiti/Blooddonorprediction","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}