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Health risk prediction for treatment of hypertension
Sandi G.a, Supangkat S.H.b, Slamet C.a
a Sunan Gunung Djati State Islamic University, Bandung, Indonesia
b Bandung Institute of Technology, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2016 IEEE.Hypertension is one of the leading causes of human deaths world. Based on data from the WHO in 2013, that there are more than 17 million people worldwide died of cardiovascular disease. While in Indonesia, based on the Basic Health Research in 2013, there were 25.8 percent of Indonesia’s population suffering from hypertension [1] [2]. This research is develops a system of prediction of prognosis in treatment of hypertension by using diagnostic data. Prediction of prognosis is used to provide advice to patient about the level of health risks associated with hypertension who suffered. This prediction system to develop business processes in predictive diagnosis of hypertension by adding functionality prognosis. The prediction is developed using methods decicion tree by using C4.5 algorithms for processing health information dataset hypertension. The output of this prediction process is the level of health risk patients. This information will be used by the patient to improve his lifestyle. Predictive testing of the system as much as 1100 instances dataset with details 880 instances as training data and 220 instances as test data. The test results of prediction on the class of hypertension are: the level of precision = 92.4% = 89.3% recall rate and accuracy rate = 94.1%. While testing the predictions of the class of risk are: the level of precision = 80.7% = 75% recall rate and accuracy rate = 80.0%.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Adding functionality,C4.5,Cardio-vascular disease,Health informations,hypertension,Prediction process,Prediction systems,Predictive testing[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]C4.5,data mining,decision tree,hypertension,machine learning,Smart health[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/CITSM.2016.7577584[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]