{ "article_id": 491, "public_id": "e4dc7ddf-ee1d-451b-9884-1abeca9c8db7", "internal_identifier": null, "source_identifier": "5:137", "status": "published", "type": "research_article", "title": "Investigation of the Performance of Classification Methods in Hepatitis Decision Support Systems", "title_secondary": null, "abstract": "In addition to developing information technologies, medical decision support systems are used to make decisions more effective,\r\nfaster, more accurate and easier to make decisions in medicine and healthcare. With data mining, which is an important part of\r\nmedical decision support systems, it is possible to obtain valuable knowledge among large-scale data, to show the linkage of\r\ndata with each other, and to foresee the future. In this study, the success of classification models, which are predictive models of\r\ndata mining used in medical decision support systems, has been investigated in determining whether hepatitis disease is fatal in\r\nterms of certain determinants. For this purpose, data set of hepatitis disease was analyzed and C4.5 decision tree, k-NN (IBK),\r\nNaive Bayes and multilayer artificial neural network (MLP) classification methods were applied to preprocessed data set through\r\nWEKA program.When the applied methods success rates of the hepatitis-diagnosed were compared, although all methods have\r\nacceptable performance, It was determined that the most successful C4.5 decision tree classification method with success rate of\r\n89.03%.The results of the study demonstrated that the C4.5 Decision Tree algorithm could be used effectively as a medical\r\ndecision support system for diagnosing hepatitis.  ", "language": "en", "english_title": null, "english_abstract": null, "doi": null, "doi_url": null, "resource_url": "https://www.set-science.com/a/e4dc7ddf-ee1d-451b-9884-1abeca9c8db7", "pdf_url": "https://www.set-science.com/a/e4dc7ddf-ee1d-451b-9884-1abeca9c8db7/pdf", "og_image": "https://www.set-science.com/assets/img/og-card-v2.png", "authors": [ { "public_id": "a491d2ee-bce3-48ce-8e1e-b251e1d15752", "given": "Sema", "family": "Güzel", "full": "Sema Güzel", "literal": null, "orcid": null, "orcid_url": null, "email": "sema.ergun@hotmail.com", "corresponding": true, "sequence": "first", "affiliations": [ { "name": "Kahramanmaraş Sütçü İmam Üniversitesi, Elektrik Elektronik Mühendisliği  ", "department": null, "country": null, "country_code": null, "ror": null, "ror_url": null } ] }, { "public_id": "b61584c7-7c2b-460e-9411-7f0c493beb1c", "given": "Ahmet", "family": "Alkan", "full": "Ahmet Alkan", "literal": null, "orcid": null, "orcid_url": null, "email": "aalkan@ksu.edu.tr", "corresponding": false, "sequence": "additional", "affiliations": [ { "name": "Kahramanmaraş Sütçü İmam Üniversitesi, Elektrik Elektronik Mühendisliği  ", "department": null, "country": null, "country_code": null, "ror": null, "ror_url": null } ] } ], "keywords": [ "Data Mining, Classification Algorithms, Decision Support Systems, Hepatitis, WEKA" ], "translations": [ { "language": "en", "title": "Investigation of the Performance of Classification Methods in Hepatitis Decision Support Systems", "abstract": "In addition to developing information technologies, medical decision support systems are used to make decisions more effective,\r\nfaster, more accurate and easier to make decisions in medicine and healthcare. With data mining, which is an important part of\r\nmedical decision support systems, it is possible to obtain valuable knowledge among large-scale data, to show the linkage of\r\ndata with each other, and to foresee the future. In this study, the success of classification models, which are predictive models of\r\ndata mining used in medical decision support systems, has been investigated in determining whether hepatitis disease is fatal in\r\nterms of certain determinants. For this purpose, data set of hepatitis disease was analyzed and C4.5 decision tree, k-NN (IBK),\r\nNaive Bayes and multilayer artificial neural network (MLP) classification methods were applied to preprocessed data set through\r\nWEKA program.When the applied methods success rates of the hepatitis-diagnosed were compared, although all methods have\r\nacceptable performance, It was determined that the most successful C4.5 decision tree classification method with success rate of\r\n89.03%.The results of the study demonstrated that the C4.5 Decision Tree algorithm could be used effectively as a medical\r\ndecision support system for diagnosing hepatitis.  \r\n", "keywords": [ "Data Mining, Classification Algorithms, Decision Support Systems, Hepatitis, WEKA" ] } ], "subjects": [], "subject_names": [], "published_date": "2018-06-23", "published_year": "2018", "received_date": null, "accepted_date": null, "volume": "2", "issue": null, "page_first": "147", "page_last": "147", "article_number": null, "conference": { "short_name": "ISAS 2018", "full_title": "Ist International Symposium on Innovative Approaches in Scientific Studies", "name": "Ist International Symposium on Innovative Approaches in Scientific Studies", "acronym": "ISAS 2018", "location": "Kemer-Antalya, Türkiye", "start_date": "2018-04-11", "end_date": null, "public_id": "1d94fec9-c4fe-4034-9157-0b205eef4e56" }, "proceedings": { "title": "Ist International Symposium on Innovative Approaches in Scientific Studies", "series_title": "Setsci conference indexing system", "publisher": "SET Technology", "isbn": null, "eisbn": null, "issn": "2618-6446", "eissn": null, "doi": null, "doi_prefix": null, "slug": "setsci-volume-2-2018-legacy-5", "public_id": "03da7b1c-cb6e-4f3b-aee4-ac41ee60fd75", "url": "https://www.set-science.com/p/setsci-volume-2-2018-legacy-5" }, "license": { "name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/", "code": "CC-BY-4.0", "open_access": true }, "copyright": { "holder": "SET Technology", "year": "2018" }, "funding_statement": null, "funders": [], "references": [], "statements": { "acknowledgements": null, "conflict_of_interest": null, "data_availability": null, "ai_disclosure": null }, "credit": [], "history": { "published": "2018-06-23" } }