{"_buckets": {"deposit": "9ce0c51d-002c-47cf-9991-6b2f4c347aaa"}, "_deposit": {"created_by": 20, "id": "8776", "owner": "20", "owners": [20], "owners_ext": {"displayname": "", "username": ""}, "pid": {"revision_id": 0, "type": "depid", "value": "8776"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/00008776"}, "author_link": [], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Data Mining with Emphasis on Exploratory Analysis", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "Data mining is an analytical tool that is used in solving critical decision making\nproblems by analyzing enormous amount of data in order to discover relationships and\nunknown patterns among variables in the data. This study focused on the investigation of\nthe application of data mining techniques based on the tuberculosis (TB) diagnosis data\nset. The required data were organized from 659 TB suspected patients who came to the\nUnion Tuberculosis Institute (UTI), Yangon during September and October 2013. This\nstudy attempted to predict whether a TB suspect has TB or not through the classification\nmodels by using decision tree method under the data mining techniques. The\nclassification task with five different algorithms was made using decision tree method. It\nwas found that the decision tree model of Algorithm I was found to be less accurate\nwhich used original data without preprocessing. The other four models which have\nperformed preprocessing task revealed a better prediction having the same accuracy.\nThus, this study proved that the decision tree method did not need the use of variable\naggregation and feature reduction. The findings indicated that Active Specific Lung\nLesion variable is the best predictor for making diagnosis about the present or absence of\nTB. The categorical value ‘Yes’ on Active Specific Lung Lesion is the most significant\npredictor of TB. Besides, the results obtained from decision tree method were compared\nwith the results from logistic regression method. It was able to show that the accuracy of\nprediction for existence of TB disease or not is the same in two methods. It has also been\nobserved that decision tree technique can provide classification rules which can identify\nthe symptoms of TB. Therefore, decision tree method is found to be advantageous for the\ncomplex problems to make correct decisions according to the application used in this\ndissertation. Moreover, an alternative decision tree model was constructed without\nincluding X-ray result (Active Specific Lung Lesion variable). Even though the results\nfrom this was less accurate model using only patient’s symptoms, the rules of this model\nwere useful for people who had not undergone medical check-up at clinics in order to the\npredict the present of TB. The classification rules provided by the decision tree model\n(without X-ray results) revealed that there is a better advantageous for the healthcare\ncenters which have no X-ray machine since these rules can be used to make the efficient\nprediction for diagnosis. By using these rules (in Appendix D), the field workers should\nencourage the patient who has high likelihood of TB positive to go to the nearest\nhealthcare center where X-ray machine, advanced technologies for diagnosis and expert\ntechnicians has."}]}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_access", "date": [{"dateType": "Available", "dateValue": "2023-03-06"}], "displaytype": "preview", "download_preview_message": "", "file_order": 0, "filename": "Aye Aye Win PhD.pdf", "filesize": [{"value": "883 KB"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_0", "mimetype": "application/pdf", "size": 883000.0, "url": {"url": "https://meral.edu.mm/record/8776/files/Aye Aye Win PhD.pdf"}, "version_id": "3884d110-c549-4ca2-a849-59f3634d9c5c"}]}, "item_1583103233624": {"attribute_name": "Thesis/dissertations", "attribute_value_mlt": [{"subitem_awarding_university": "Yangon University of Economics"}]}, "item_1583105942107": {"attribute_name": "Authors", "attribute_value_mlt": [{"subitem_authors": [{"subitem_authors_fullname": "Aye Aye Win"}]}]}, "item_1583108359239": {"attribute_name": "Upload type", "attribute_value_mlt": [{"interim": "Other"}]}, "item_1583108428133": {"attribute_name": "Publication type", "attribute_value_mlt": [{"interim": "Dissertation"}]}, "item_1583159729339": {"attribute_name": "Publication date", "attribute_value": "2015-06-01"}, "item_title": "Data Mining with Emphasis on Exploratory Analysis", "item_type_id": "21", "owner": "20", "path": ["1582965742757"], "permalink_uri": "https://meral.edu.mm/records/8776", "pubdate": {"attribute_name": "Deposit date", "attribute_value": "2023-03-06"}, "publish_date": "2023-03-06", "publish_status": "0", "recid": "8776", "relation": {}, "relation_version_is_last": true, "title": ["Data Mining with Emphasis on Exploratory Analysis"], "weko_shared_id": -1}