Exploratory Study of Kohonen Network for Human Health State Classification

Hamijah Mohd Rahman (Universiti Tun Hussein Onn Malaysia, Johor, Malaysia)
Nureize Arbaiy (Universiti Tun Hussein Onn Malaysia, Johor, Malaysia)
Muhammad Shukeri Che Lah (Universiti Tun Hussein Onn Malaysia, Johor, Malaysia)
Norlida Hassan (Universiti Tun Hussein Onn Malaysia, Johor, Malaysia)

JOIV : International Journal on Informatics Visualization
Vol 2, No 3-2 (2018): The Diversity in Information Systems

Kohonen Network is an unsupervised learning which forms clusters from patterns that share common features and group similar patterns together. This network are commonly uses grids of artificial neurons which connected to all the inputs. This paper presents an exploratory study of Kohonen Neural Network to classify human health state. Neural Connection tool is used to generate the result based on Kohonen learning algorithm. Procedural steps are provided to assist the implementation of the Kohonen Network. The result shows that side 2 is more appropriate for this problem with efficient learning rate 1.0. It gives good distribution for training and test patterns. Study to the variation of dataset’s size will be considered in the near future to evaluate the performance of the network.