University of Bahrain
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A Novel Holistic Disease Prediction Tool Using Best Fit Data Mining Techniques

Show simple item record Diwani, Salim A. Yonah, Zaipuna O. 2018-07-09T07:07:06Z 2018-07-09T07:07:06Z 2017-03-01
dc.identifier.issn 2210-142X
dc.description.abstract Given that, today, the healthcare ecosystem is an information rich industry, there is an increasing demand for data mining (DM) tools to improve the quantity and quality of delivered healthcare; especially in handling patients suffering from deadly diseases such as HIV, Breast Cancer, Diabetes, Tuberculosis (TB), Heart diseases and Liver disorder. Given the fatality nature of these diseases when they remain undetected until at advanced stages, there remains a demand for best classifier tools to assist in diagnosing, detecting and treatment of these life-threatening diseases at their early stages. Complementary to this demand is the fact that the healthcare industry today generates large amounts of complex data about patients, hospital resources and disease diagnosis. Consequently, the healthcare ecosystem is warehousing large amount of medical data, which is an asset for healthcare organizations if properly utilized. The large amount of patient and disease related data could be processed and analyzed for knowledge extraction that enables support for cost savings and decision making towards delivery of timely and quality healthcare. In this paper, we report on an ongoing research work to develop and test a holistic DM disease prediction (Diagnosis and prognosis) tool, equipped with processes for preprocessing patients’ data and a learning procedure for selecting a disease-specific best classifier, for disease prediction and delivery of speedy and cost effective diagnostic interventions and patient follow up in a hospital environment. As diseases are diagnosed, the predictive tool helps medical doctors in decision-making about what disease case it is and suggests possible treatment strategies within a much-reduced time. Test results for breast cancer and HIV data sets are reported. Achieved from the reported work are classification accuracies of 97.0752% (Classifier acting singly); 97.6323% (fusion of three classifiers). These results are better than those reported in the literature. The results show that the proposed DM disease prediction tool has potential to greatly impact on current patient management, care and future interventions against deadly diseases. en_US
dc.language.iso en_US en_US
dc.publisher University of Bahrain en_US
dc.rights Attribution-NonCommercial-ShareAlike 4.0 International *
dc.rights.uri *
dc.subject Healthcare delivery en_US
dc.subject Data mining en_US
dc.subject Electronic medical records en_US
dc.subject HIV en_US
dc.subject Breast cancer en_US
dc.subject Diabetes en_US
dc.subject Tuberculosis en_US
dc.subject Heart diseases en_US
dc.subject Liver disorder en_US
dc.title A Novel Holistic Disease Prediction Tool Using Best Fit Data Mining Techniques en_US
dc.type Article en_US
dc.volume 06
dc.issue 02
dc.pagestart 63
dc.pageend 72
dc.source.title International Journal of Computing and Digital Systems
dc.abbreviatedsourcetitle IJCDS

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