A Web/Mobile Decision Support System to Improve Medical Diagnosis Using a Combination of K-Mean Clustering and Fuzzy Logic
This research provides a system that integrates the work of data mining and expert system for different tasks in the process of medical diagnosis, and provides detailed steps to the process of reaching a diagnosis based on the described symptoms and mapping them with existing diagnosis available on the web or on a cloud of medical knowledge based, aggregate these data in a fuzzy manner and produce a satisfactory diagnosis of the persisting problem. The mobile phone interface would make the system user-friendly and provides mobility and accessibility to the user, while posting updates and reading in details the steps that led to the decision or diagnosis that is reached by the K-mean and the fuzzy logic inference engine.The achieved results indicate a promising diagnosis performance of the system as it achieved 90% accuracy and 92.9% F-Score.
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