Prediction and Decision Making in Health Care using Data Mining

Tendency for data mining application in healthcare today is great, because healthcare sector is rich with information, and data mining is becoming a necessity. Healthcare organizations produce and collect large volumes of information on daily basis. Use of information technologies allows automatization of processes for extraction of data that help to get interesting knowledge and regularities, which means the elimination of manual tasks and easier extraction of data directly from electronic records, transferring onto secure electronic system of medical records which will save lives and reduce the cost of the healthcare services, as well and early discovery of contagious diseases with the advanced collection of data. Data mining can enable healthcare organizations to predict trends in the patient conditions and their behaviors, which is accomplished by data analysis from different perspectives and discovering connections and relations from seemingly unrelated information. Raw data from healthcare organizations are voluminous and heterogeneous. They need to be collected and stored in the organized forms, and their integration enables forming of hospital information system. Healthcare data mining provides countless possibilities for hidden pattern investigation from these data sets. These patterns can be used by physicians to determine diagnoses, prognoses and treatments for patients in healthcare organizations.DOI: http://dx.doi.org/10.11591/ijphs.v1i2.138

Integrated Framework of Knowledge Discovery and Knowledge Management for E-health In Saudi Arabia: Supporting Citizens with Diabetes Mellitus

Saudi Arabia experiences insufficient effort in terms of patients’ education in relation to a number of prevalent diseases, including diabetes mellitus, musculoskeletal disorders and upper respiratory tract infections. In addition, the number of studies related to e-health initiatives to support patients in the Kingdom are limited and only benefit patients of a few hospitals. This situation leads to deficient application of self-management and education strategies to empower patients to manage their diseases. Unfortunately, such a deficiency can affect the health status in the Kingdom negatively as diabetes mellitus is reported as the first cause of death in the Kingdom among all other prevalent diseases. Although knowledge management has been proven to be a valuable approach to sharing knowledge and educating users to manage their illnesses, it has not been implemented appropriately to support the increasing number of diabetic citizens in Saudi Arabia. In this research, knowledge management is integrated with knowledge discovery to support specific needs of the diabetic community in the Kingdom. Such an integration constitutes an e-health initiative to support diabetic citizens and healthcare professionals to manage this expanding illness in Saudi Arabia. Knowledge discovery is implemented through data mining to elicit useful knowledge related to specific diabetes complications encountered by diabetic citizens in the Kingdom. The integrated framework applies the SECI model to capture and disseminate useful diabetes self-management and educational expertise to support the management of diabetes complications. This integrated approach to knowledge management and knowledge discovery has provided a valuable tool implemented in terms of a web portal. This has facilitated the exchange and dissemination of tacit and explicit knowledge of the diabetic community in the forms of strategies, guidelines and best practices. It has also overcome the issues faced by the organisational and national cultures affecting knowledge management practice in Saudi Arabia