{"_buckets": {"deposit": "d56112e9-4403-460c-a7b3-213128ec05ea"}, "_deposit": {"id": "5058", "owners": [], "pid": {"revision_id": 0, "type": "recid", "value": "5058"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/5058", "sets": ["1597824273898", "user-ucsy"]}, "communities": ["ucsy"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Outliers Detection and Analyzing for Diabetic and Non-Diabetic Patients by Using Two-Phase Clustering", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "Outliers are the set of objects that areconsiderably dissimilar from other values in arandom sample from a population. Outliers areimportant because they can change the results ofdata analysis. In this paper, two-phase clusteringapproach is used to fulfil the detection ofabnormal diabetic and non-diabetic patients. Thepatients’ data is taking from one of datasetsdonated to UCI machine learning repository,Pima Indians diabetes dataset from NationalInstitute of Diabetes and Digestive and KidneyDiseases. In the phase-1, patients’ data areclustered according to their similar features. Inthe phase-2, a minimum spanning tree (MST) isconstructed to detect clusters with outliers. Theconclusion remarks on resulted abnormal patientsare given by the expert researcher of diabetesdisease and analyses on attributes\u0027 features arealso presented in this paper. Thus, it is intended todetect abnormal patients of positive or negative indiabetes test result and to illustrate analyses onthem."}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value": []}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_access", "date": [{"dateType": "Available", "dateValue": "2019-07-18"}], "displaytype": "preview", "download_preview_message": "", "file_order": 0, "filename": "53_PDFsam_PSC_final proof.pdf", "filesize": [{"value": "244 Kb"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_free", "mimetype": "application/pdf", "size": 244000.0, "url": {"url": "https://meral.edu.mm/record/5058/files/53_PDFsam_PSC_final proof.pdf"}, "version_id": "86369b52-5200-43ee-b4d1-101f1d1b2f85"}]}, "item_1583103131163": {"attribute_name": "Journal articles", "attribute_value_mlt": [{"subitem_issue": "", "subitem_journal_title": "Eighth Local Conference on Parallel and Soft Computing", "subitem_pages": "", "subitem_volume": ""}]}, "item_1583103147082": {"attribute_name": "Conference papers", "attribute_value_mlt": [{"subitem_acronym": "", "subitem_c_date": "", "subitem_conference_title": "", "subitem_part": "", "subitem_place": "", "subitem_session": "", "subitem_website": ""}]}, "item_1583103211336": {"attribute_name": "Books/reports/chapters", "attribute_value_mlt": [{"subitem_book_title": "", "subitem_isbn": "", "subitem_pages": "", "subitem_place": "", "subitem_publisher": ""}]}, "item_1583103233624": {"attribute_name": "Thesis/dissertations", "attribute_value_mlt": [{"subitem_awarding_university": "", "subitem_supervisor(s)": [{"subitem_supervisor": ""}]}]}, "item_1583105942107": {"attribute_name": "Authors", "attribute_value_mlt": [{"subitem_authors": [{"subitem_authors_fullname": "Htwe, Hsu Mon"}, {"subitem_authors_fullname": "Phyu, Aye Lei Lei"}]}]}, "item_1583108359239": {"attribute_name": "Upload type", "attribute_value_mlt": [{"interim": "Publication"}]}, "item_1583108428133": {"attribute_name": "Publication type", "attribute_value_mlt": [{"interim": "Article"}]}, "item_1583159729339": {"attribute_name": "Publication date", "attribute_value": "2017-12-27"}, "item_1583159847033": {"attribute_name": "Identifier", "attribute_value": "http://onlineresource.ucsy.edu.mm/handle/123456789/950"}, "item_title": "Outliers Detection and Analyzing for Diabetic and Non-Diabetic Patients by Using Two-Phase Clustering", "item_type_id": "21", "owner": "1", "path": ["1597824273898"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000005058", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2019-07-18"}, "publish_date": "2019-07-18", "publish_status": "0", "recid": "5058", "relation": {}, "relation_version_is_last": true, "title": ["Outliers Detection and Analyzing for Diabetic and Non-Diabetic Patients by Using Two-Phase Clustering"], "weko_shared_id": -1}