Exploring the Interrelationship of Risk Factors for Supporting eHealth Knowledge-Based System

In developing countries like Africa, the physician-to-population ratio is below the World Health Organization (WHO) minimum recommendation. Because of the limited resource setting, the healthcare services did not get the equity of access to the use of health services, the sustainable health financing, and the quality of healthcare service provision. Efficient and effective teaching, alerting, and recommendation system are required to support the activi - ties of the healthcare service. To alleviate those issues, creating a competitive eHealth knowl-edge-based system (KBS) will bring unlimited benefit. In this study, Apriori techniques are applied to malaria dataset to explore the degree of the association of risk factors. And then, integrate the output of data mining (i.e., the interrelationship of risk factors) with knowledge- based reasoning. Nearest neighbor retrieval algorithms (for retrieval) and voting method (to reuse tasks) are used to design and deliver personalized knowledge-based system.