DESIGN AND IMPLEMENTATION OF AN INTELLIGENT DECISIONS SUPPORT SYSTEM FOR TYPHOID AND MALARIA DIAGNOSIS
Abstract
ABSTRACT
Typhoid and malaria are two prevalent diseases in many tropical regions, often presenting similar symptoms that can lead to misdiagnosis and delayed treatment. This project aims to design and implement an intelligent decision support system (IDSS) for accurate diagnosis of typhoid and malaria. The system utilizes machine learning algorithms and expert knowledge to analyze patient symptoms, medical history, and laboratory results. A user-friendly interface allows healthcare professionals to input patient data and receive diagnostic recommendations. The IDSS was evaluated using a dataset of patient records and demonstrated high accuracy in distinguishing between typhoid and malaria. The system has the potential to improve diagnostic accuracy, reduce misdiagnosis rates, and enhance patient outcomes in resource-limited settings.
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