Data Mining Techniques Implementation To Improve Healthcare Among Diabetic Patients
Medical data mining is an emergent field and, on overcoming its facing challenges such as privacy of documentation and ethical use of information about patients, voluminous and heterogeneous data, and imprecise and erroneous data, medical data mining can be as powerful as that in any other common fi...
محفوظ في:
| المؤلف الرئيسي: | |
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| منشور في: |
2016
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| الموضوعات: | |
| الوصول للمادة أونلاين: | http://bspace.buid.ac.ae/handle/1234/974 |
| الوسوم: |
إضافة وسم
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| _version_ | 1862980619307319296 |
|---|---|
| author | AlBanna, Ghania Aref |
| author_facet | AlBanna, Ghania Aref |
| author_role | author |
| dc.creator.none.fl_str_mv | AlBanna, Ghania Aref |
| dc.date.none.fl_str_mv | 2016-12 2017-03-02T13:17:48Z 2017-03-02T13:17:48Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | 2014128042 http://bspace.buid.ac.ae/handle/1234/974 |
| dc.language.none.fl_str_mv | en |
| dc.publisher.none.fl_str_mv | The British University in Dubai (BUiD) |
| dc.subject.none.fl_str_mv | data mining healthcare diabetic patients |
| dc.title.none.fl_str_mv | Data Mining Techniques Implementation To Improve Healthcare Among Diabetic Patients |
| dc.type.none.fl_str_mv | Dissertation |
| description | Medical data mining is an emergent field and, on overcoming its facing challenges such as privacy of documentation and ethical use of information about patients, voluminous and heterogeneous data, and imprecise and erroneous data, medical data mining can be as powerful as that in any other common field such as ecommerce and marketing. Traditional research could not overcome completely these challenges and only hypotheses based on anthropological approaches are tested. Unlike traditional research, this dissertation discusses predictive analysis and knowledge discovery of trends and patterns from databases in the medical field. Retrieval of clinical medical data is helpful in conducting different learning techniques. Performance of different classification techniques is compared and ensemble learning of best classifiers is tested. The analysis showed that ensemble learning via bagging predicts best the percentage of diabetic adolescents who are most prone to hospital readmission and more susceptible to join the “Diabetic Self-Management Educational Support Program”. This predictive classification helps in leveraging the healthy psychological status of the patients (social and medical), reducing readmission costs (economic), and pre-hypothesizing (scientific) relationships between different parameters based on different patterns and trends predicted by machine learning techniques. |
| id | budr_c324e9f847ab86e2fed23c9187c0bd12 |
| identifier_str_mv | 2014128042 |
| language_invalid_str_mv | en |
| network_acronym_str | budr |
| network_name_str | The British University in Dubai repository |
| oai_identifier_str | oai:bspace.buid.ac.ae:1234/974 |
| publishDate | 2016 |
| publisher.none.fl_str_mv | The British University in Dubai (BUiD) |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Data Mining Techniques Implementation To Improve Healthcare Among Diabetic PatientsAlBanna, Ghania Arefdata mininghealthcarediabetic patientsMedical data mining is an emergent field and, on overcoming its facing challenges such as privacy of documentation and ethical use of information about patients, voluminous and heterogeneous data, and imprecise and erroneous data, medical data mining can be as powerful as that in any other common field such as ecommerce and marketing. Traditional research could not overcome completely these challenges and only hypotheses based on anthropological approaches are tested. Unlike traditional research, this dissertation discusses predictive analysis and knowledge discovery of trends and patterns from databases in the medical field. Retrieval of clinical medical data is helpful in conducting different learning techniques. Performance of different classification techniques is compared and ensemble learning of best classifiers is tested. The analysis showed that ensemble learning via bagging predicts best the percentage of diabetic adolescents who are most prone to hospital readmission and more susceptible to join the “Diabetic Self-Management Educational Support Program”. This predictive classification helps in leveraging the healthy psychological status of the patients (social and medical), reducing readmission costs (economic), and pre-hypothesizing (scientific) relationships between different parameters based on different patterns and trends predicted by machine learning techniques.The British University in Dubai (BUiD)2017-03-02T13:17:48Z2017-03-02T13:17:48Z2016-12Dissertationapplication/pdf2014128042http://bspace.buid.ac.ae/handle/1234/974enoai:bspace.buid.ac.ae:1234/9742021-10-17T12:42:27Z |
| spellingShingle | Data Mining Techniques Implementation To Improve Healthcare Among Diabetic Patients AlBanna, Ghania Aref data mining healthcare diabetic patients |
| title | Data Mining Techniques Implementation To Improve Healthcare Among Diabetic Patients |
| title_full | Data Mining Techniques Implementation To Improve Healthcare Among Diabetic Patients |
| title_fullStr | Data Mining Techniques Implementation To Improve Healthcare Among Diabetic Patients |
| title_full_unstemmed | Data Mining Techniques Implementation To Improve Healthcare Among Diabetic Patients |
| title_short | Data Mining Techniques Implementation To Improve Healthcare Among Diabetic Patients |
| title_sort | Data Mining Techniques Implementation To Improve Healthcare Among Diabetic Patients |
| topic | data mining healthcare diabetic patients |
| url | http://bspace.buid.ac.ae/handle/1234/974 |