Developing HIV/AIDS Patient Profile Model Using K-Means Clustering Method
Abstract
Dr. Pirngadi is one of the Regional General Hospitals in North Sumatra that handles services for HIV/AIDS patients. Patients who actively take ARV therapy every month are low, namely around 20%. This will hurt efforts to prevent and transmit HIV/AIDS-, because people infected with HIV become carriers and transmitters of the HIV throughout HIVes,HIV though they don't feel sick and look healthy, the sufferer still carries HIV. To be able to carry out more effective and efficient handling of the prevention and control from HIV/AIDS transmission, it is very important for the government and related parties, such as the Health Office, Social Services, and Hospital management, especially in VCT/CST (Voluntary Counseling and Testing) services. /Care Support Treatment) to find out about understanding patient profiles, the knowledge possessed to understand, predict, and prevent the development of HIV/AIDS disease transmission is very important. This knowledge can be used by the government to carry out programs that can prevent and break the chain of transmission of the HIV/AIDS virus as early as possible and help those involved in health services to become more familiar with the situation of their patients and can be used as early knowledge in the decision-making process to preventive actions in terms of improving providing health services to patients, increasing the level of public health, improving people's welfare and many other benefits that can be obtained found from the results data mining. This research takes the service area at RSUD Dr. Pirngadi Medan as one of the research domains in the field of data mining with data sources from RSUD Dr. Pirngadi Medan. This is done as information is known to the VCT/CST services at RSDU Dr. Pirngadi Medan. With data groupings like this, it is hoped that the Government or related agencies can create programs and implement them so that they can prevent and overcome the spread of HIV/AIDS in Indonesia. It is on this basis that the authors are interested in taking the title of the study regarding the formation of a patient profile model using the K-Mean clustering method.
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