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All Journal JOIV : International Journal on Informatics Visualization
Hamijah Mohd Rahman
Universiti Tun Hussein Onn Malaysia, Johor, Malaysia
Computer Science & IT

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An Expert System for Pneumococcal Prognosis Nurnadzirah Othman; Nureize Arbaiy; Hamijah Mohd Rahman
JOIV : International Journal on Informatics Visualization Vol 2, No 3-2 (2018): The Diversity in Information Systems
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (959.535 KB) | DOI: 10.30630/joiv.2.3-2.142

Abstract

Threats and viruses are particularly alarming for children with low immunization levels. Pneumococcal disease is the world's most important cause of child death and has claimed many lives. Since awareness of the dangers of Pneumococcal viruses among parents is low in Malaysia, preventive measures such as vaccine intake cannot be done comprehensively. Hence, in order to communicate information about Pneumococcal disease, a pneumococcal disease diagnosis system for children is developed. This system employs expert system method and apply forward chaining technique for its reasoning. Knowledge base of the system is stored in the database for data management. This alternative system allows access to information as well as early diagnosis of early symptoms can be detected. This system is expected to benefit users in terms of knowledge sharing, and self-checking on their body condition, especially parents, to prevent any possible diseases that may infect children's.
Exploratory Study of Kohonen Network for Human Health State Classification Hamijah Mohd Rahman; Nureize Arbaiy; Muhammad Shukeri Che Lah; Norlida Hassan
JOIV : International Journal on Informatics Visualization Vol 2, No 3-2 (2018): The Diversity in Information Systems
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (982.301 KB) | DOI: 10.30630/joiv.2.3-2.143

Abstract

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.
Co-Authors Muhammad Shukeri Che Lah Norlida Hassan Nureize Arbaiy Nurnadzirah Othman