The Development of Integrated Medical Expert System for Diabetes Mellitus Patient

Abstract

Diabetes Mellitus (DM) is a degenerative disease that has many causative factors. DM patients cannot recover completely but they can maintain the stability of their glucose level by following a healthy lifestyle. The goal of this research is to make an integrated system for people to promote a healthy lifestyle. This system also allows people to monitor the changes in their daily health conditions regularly. Main features in this system diagnose, treatment, guidelines for a healthy lifestyle, including a feature for counting and saving daily calorie consumption and physical activity. The system using the backward inference method to get the conclusion from a set of rules in the knowledge base. The verification using a query, validation with using recall and precision, and usability testing using a questionnaire, was conducted to test the performance of the system. The result of the testing shows that the system gives a good response to all queries given and the user also satisfied with the system. The recall value is 1 with the precision is 0.658. This precision value also promotes the inference process used in the system, which every user will suspect has DM when they start to use the diagnose feature.

Enhancing Precision of Telemonitoring of COVID-19 Patients through Expert System Based on IoT Data Elaboration

“…To the best of our knowledge, our proposal represents the first application of artificial intelligence for the aforementioned specific purpose. With respect to classification performance, the achieved results are in line with other AI applications presented in the literature, even if aimed at different tasks in the medical context [42,55]. Furthermore, considering the unweighted confusion matrix (Figure 5a), it is also worth noticing that the majority of misclassifications occurred above the main diagonal, and thus, in a region where their impact was relatively less significant, whereas only in very few cases were there an underestimation of the patient risk of at most one class.…”