Tropical Diseases Identification Using Neural Network Adaptive Resonance Theory 2

The diagnosis of tropical diseases carried out by a doctor to determine medical treatment for patients must be done carefully and accurately. Errors in diagnosis can be fatal and dangerous for patients. Tropical diseases are discussed in this study are malaria, dengue fever and typhoid. These three...

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Bibliographic Details
Published in2018 6th International Conference on Cyber and IT Service Management (CITSM) pp. 1 - 4
Main Authors Rosnelly, Rika, Wahyuni, Linda
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2018
Subjects
Adaptive systems
Artificial neural networks
component
Diseases
formatting
Medical diagnostic imaging
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Summary:The diagnosis of tropical diseases carried out by a doctor to determine medical treatment for patients must be done carefully and accurately. Errors in diagnosis can be fatal and dangerous for patients. Tropical diseases are discussed in this study are malaria, dengue fever and typhoid. These three diseases have some similar symptoms, so doctors often make mistakes in conducting the initial diagnosis for the patients. To solve this problem, the authors conducted a study by implementing Artificial Neural Networks Adaptive Resonance Theory 2 which can be done simply by entering the data of the same symptoms of the three tropical diseases. This data is then trained by using the model and compared with the data of symptoms experienced by the patients to determine the tropical diseases suffered. The advantage of this method is the use of the new data does not defect the old data [6]. The application of the method has been tested by using several real cases with a success rate of 91,67 %.
DOI:10.1109/CITSM.2018.8674364