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Author A. B. Adeyemo ♦ Akinwonmi, A. E.
Source CiteSeerX
Content type Text
File Format PDF
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Adeyemo Akinwonmi ♦ Diabetes Mellitus Using Ann Model ♦ Diabetes Mellitus ♦ Comprehensive Medical History ♦ Classified Record ♦ Predictive Network ♦ Risk Factor ♦ Medical Worker ♦ Peripheral Vascular Disease ♦ Predictive Neural Network ♦ Combined Diagnosis ♦ Diagnosis Process ♦ Sufficient Insulin ♦ Many Medical Condition ♦ Classifier Network ♦ Common Medical Condition ♦ Work Artificial Neural Network Model ♦ Rapid Diagnosis ♦ Various Neural Network Architecture ♦ Measurable Parameter ♦ Treatment Neural Network ♦ Data Set ♦ Thorough Physical Examination ♦ Ultimate Intention ♦ Coronary Artery Disease ♦ Disease Diagnosis ♦ Chronic Disease ♦ Grnn Pnn Network ♦ Body Cannot ♦ Neural Network Model
Abstract Diabetes mellitus is a chronic disease which occurs when the pancreas does not produce sufficient insulin, or when the body cannot effectively use the insulin it produces. It is an important and relatively common medical condition and is a risk factor for many other medical conditions like stroke, peripheral vascular disease and coronary artery disease. Physicians have to elicit a comprehensive medical history and thorough physical examination before diabetes mellitus can be suspected. In this process a lot of data has been collected on the diseases diagnosis and treatment. In this work Artificial Neural Network models were developed using both classification and predictive neural networks for the rapid diagnosis of diabetes mellitus. Both neural network models were able to learn the problem with the predictive network giving a better performance of 84 % correctly classified records as opposed to 76 % achieved by the classifier network on the same data set. A combined Diagnosis and Treatment neural network was also modeled using various neural network architectures. The GRNN/PNN network gave the best result out of the three architectures used. The other networks were unable to model the problem. The ultimate intention is to assist medical workers in the diagnosis process using physically measurable parameters (symptoms).
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study