%0 Journal Article %1 noauthororeditor %A C., Omeye Emmanuel C. | Ngene John N. | Dr. Anyaragbu Hope U. | Dr. Ozioko Ekene | Dr. Iloka Bethram C. | Prof. Inyiama Hycent %D 2023 %J INTERNATIONAL JOURNAL OF TREND IN SCIENTIFIC RESEARCH AND DEVELOPMENT %K Diabetes; Learning MSE Machine Network; Neural Peripheral R System; expertise %N 7 %P 429-438 %T Development of a Hybrid Dynamic Expert System for the Diagnosis of Peripheral Diabetes and Remedies using a Rule Based Machine Learning Technique %U https://www.ijtsrd.com/computer-science/other/52356/development-of-a-hybrid-dynamic-expert-system-for-the-diagnosis-of-peripheral-diabetes-and-remedies-using-a-rulebased-machine-learning-technique/omeye-emmanuel-c %V 6 %X 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. Omeye Emmanuel C. | Ngene John N. | Dr. Anyaragbu Hope U. | Dr. Ozioko Ekene | Dr. Iloka Bethram C. | Prof. Inyiama Hycent C. "Development of a Hybrid Dynamic Expert System for the Diagnosis of Peripheral Diabetes and Remedies using a Rule-Based Machine Learning Technique" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-7 , December 2022, URL: https://www.ijtsrd.com/papers/ijtsrd52356.pdf Paper URL: https://www.ijtsrd.com/computer-science/other/52356/development-of-a-hybrid-dynamic-expert-system-for-the-diagnosis-of-peripheral-diabetes-and-remedies-using-a-rulebased-machine-learning-technique/omeye-emmanuel-c

@article{noauthororeditor, abstract = {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. Omeye Emmanuel C. | Ngene John N. | Dr. Anyaragbu Hope U. | Dr. Ozioko Ekene | Dr. Iloka Bethram C. | Prof. Inyiama Hycent C. "Development of a Hybrid Dynamic Expert System for the Diagnosis of Peripheral Diabetes and Remedies using a Rule-Based Machine Learning Technique" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-7 , December 2022, URL: https://www.ijtsrd.com/papers/ijtsrd52356.pdf Paper URL: https://www.ijtsrd.com/computer-science/other/52356/development-of-a-hybrid-dynamic-expert-system-for-the-diagnosis-of-peripheral-diabetes-and-remedies-using-a-rulebased-machine-learning-technique/omeye-emmanuel-c }, added-at = {2023-01-07T13:10:38.000+0100}, author = {C., Omeye Emmanuel C. | Ngene John N. | Dr. Anyaragbu Hope U. | Dr. Ozioko Ekene | Dr. Iloka Bethram C. | Prof. Inyiama Hycent}, biburl = {https://www.bibsonomy.org/bibtex/29e78c820dc3715e07170dce65b9dd493/ijtsrd}, interhash = {ce2974a217b9c1682297f85b2a549593}, intrahash = {9e78c820dc3715e07170dce65b9dd493}, issn = {2456-6470}, journal = {INTERNATIONAL JOURNAL OF TREND IN SCIENTIFIC RESEARCH AND DEVELOPMENT}, keywords = {Diabetes; Learning MSE Machine Network; Neural Peripheral R System; expertise}, language = {english}, month = jan, number = 7, pages = {429-438}, timestamp = {2023-01-07T13:10:38.000+0100}, title = {Development of a Hybrid Dynamic Expert System for the Diagnosis of Peripheral Diabetes and Remedies using a Rule Based Machine Learning Technique }, url = {https://www.ijtsrd.com/computer-science/other/52356/development-of-a-hybrid-dynamic-expert-system-for-the-diagnosis-of-peripheral-diabetes-and-remedies-using-a-rulebased-machine-learning-technique/omeye-emmanuel-c}, volume = 6, year = 2023 }