This paper presents the development of a hybrid dynamic expert system for the diagnosis of peripheral diabetes and remedies using a rule-based machine learning technique. The aim was to develop a solution to the risk factors of peripheral diabetes. The methodology applied in this study is the experimental method, and the software design methodology used was the agile methodology. Data was collected from Nnamdi Azikiwe University Teaching Hospitals (NAUTH) and the Lagos State University Teaching Hospital (LASUTH) for patients between the ages of 28-87years suffering from peripheral neuropathy. Other methods used were data integration by applying uniform data access (UDA) technique, data processing using Infinite Impulse Response Filter (IIRF), data extraction with a computerized approach, machine learning algorithm with Dynamic Feed Forward Neural Network (DFNN), rule-base algorithm. The modeling of the hybrid dynamic expert system and remedies was achieved using the DFNN for the detection of DPN and a rule-based model for remedies and recommendations. The models were implemented with MATLAB and Java programming languages. The result when evaluated achieved a Mean Square Error (MSE) of 4.9392e-11 and Regression (R) of 0.99823. The implication of the result showed that the peripheral diabetes detection model correctly learns the peripheral diabetes attributes and was also able to correctly detect peripheral diabetes in patients. The model when compared with other sophisticated models also showed that it achieved a better regression score. The reason was due to the appropriate steps used in the data preparation such as integration and the use of IIFR filter, feature extraction, and the deep configuration of the regression model.
Expert System; Peripheral Diabetes; Neural Network; Machine Learning, MSE, R
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