Early self-diagnosis of dengue symptoms using fuzzy and data mining approach

Dengue fever (DF) is of huge public health problem that causes mortality and morbidity worldwide. Currently, dengue fever is the most crucial infectious disease in Malaysia. Delay in detection of dengue disease could lead to life threatening complications and increase fatality rate. Therefore, this research aimed to develop an accurate model that could better detect early signs and symptoms of dengue fever and a practical system for self-notification of the disease. Two techniques were applied to give early self-notification to the patients whether they are suspected to have dengue fever or not namely the fuzzy expert system and data mining technique. The rules of dengue diagnosis built based on an interview with doctor and those rules will be applied in an expert system using a fuzzy logic. However, before applying the extracted rules, the accuracy of rules was tested by data mining tool. The experimental results show that the fuzzy logic approach with data mining had improved the accuracy and produce a reliable result for self-diagnosis of dengue symptoms.