Prediction Model of Cardiovascular Diseases Using ANFIS Sugeno

Cardiovascular diseases are among the killer diseases in the world. The diseases caused a lot of death, and disabilities, and contributed to high costs of treatment. Early treatment of cardiovascular diseases by knowing the risk factors for disease susceptibility will facilitate treatment and healing. This study aims to develop a cardiovascular disease prediction model using the Sugeno's adaptive neuro-fuzzy inference system (ANFIS). The grid partition and sub-clustering were used in the developed ANFIS. The data set comprises clinical data from UCI Global Data. The result analysis of the prediction model generated Fuzzy Inference System (FIS) uses grid partition with optimization backpropagation, grid partition with optimization hybrid, sub-clustering with optimization backpropagation, sub-clustering with optimization hybrid values of root mean square error are 0.7059, 0.2579, 0.7071, and 0.2576, respectively.