Research Article
Soft Neuro Computing GUI-based Diabetes Mellitus
@INPROCEEDINGS{10.4108/eai.23-4-2018.2277601, author={Agus Rusgiyono and Hasbi Yasin and Abdul Hoyyi and Rezzy Eko Caraka}, title={Soft Neuro Computing GUI-based Diabetes Mellitus}, proceedings={Joint Workshop KO2PI and The 1st International Conference on Advance \& Scientific Innovation}, publisher={EAI}, proceedings_a={ICASI}, year={2018}, month={7}, keywords={soft computing; ffnn; diabetes mellitus; classification}, doi={10.4108/eai.23-4-2018.2277601} }- Agus Rusgiyono
Hasbi Yasin
Abdul Hoyyi
Rezzy Eko Caraka
Year: 2018
Soft Neuro Computing GUI-based Diabetes Mellitus
ICASI
EAI
DOI: 10.4108/eai.23-4-2018.2277601
Abstract
Health is a vital aspect of life. According to this, we need to improve public health due to an unhealthy lifestyle which may lead to the occurrence of various kinds of diseases. Diabetes Mellitus (DM) is one negative impacts left by an unhealthy lifestyle which is caused by high blood sugar levels. Numbers of diabetes patients get late treatments due to delayed identification on them. In fact, if a diagnosis report conducted earlier, the treatment can be given earlier as well, and bad conditions may be avoided. Therefore, a system identifying diabetes is needed so that the disease can be detected as fast, accurate, and early as possible. To tackle this issue, we aimed to design soft computing system for early detection of diabetes mellitus by using neural network and binary sigmoid activation function. In a nutshell, The accuracy level of the DM detection was 92.44%.
