TOWARDS CEREBRAL PALSY DIAGNOSIS: AN-ONTOLOGY BASED APPROACH

Cerebral Palsy (CP) is one of the most complicated disabilities which is a permanent motor disorder causing mental and physical disabilities. Different reports published by different health organizations asked for researches on CP disability in order to improve diagnosis. Globally, there are different researches conducted to improve CP diagnosis, but most of those studies do not diagnose CP in children’s early ages, which limit the treatment impact. This paper report on a research conducted to develop an ontology-based approach to diagnose children with CP in early ages. In this paper, Ontology was used to represent CP domain. Then, a set of manually built rules have been optimized through a knowledge-based survey to be used in CP diagnosis. The proposed approach improves CP diagnosis and by consequence positively reflects physical therapy treatment. The proposed approach was evaluated by a real dataset consisted of 70 pre-diagnosed cases, where 84% correctly diagnosed

Towards Rules Induction Optimization for Cerebral Palsy Diagnosis: An Empirical Study

According to the World Health Organization, there are about 15% of universe population have some forms of disability. One of these disabilities is Cerebral Palsy CP which is a permanent motor disorder that appears in early age causing mental and physical disabilities. Based on WHO, between 1.5 to 2.8 cases per 1000 birth have CP Disability in the world. In Gaza Strip, 2.6% of population has some forms of disability. The children under 18 years with CP disability represent 3.1% of disability population in Gaza Strip. Based on domain experts' opinion, there are very little studies that target disability in Gaza Strip especially Cerebral Palsy. In this paper we propose a new approach for rules induction and optimization for Cerebral Palsy diagnosis based on symptoms that appear on the children aged from 1 day to 3 years. We used knowledge-based survey technique to extract tacit knowledge related to disability