The Prediction, Diagnosis and Treatment of Diabetes Mellitus Using an Intelligent Decision Support S
Authors:
ADEKUNLE Yinka
Publication Type: Journal article
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Abstract
In recent times, diabetes mellitus has become a highly dreaded illness which has negatively impacted the health of its victims the world over. This paper proposes a framework for the prediction, diagnosis and treatment of diabetes mellitus so as to provide a long sought proactive solution to the diabetes menace. In the framework, the patient information is fed into the clinical decision support system through the user interface; the knowledge base stores the rules and data to be used by the system and the pattern classification/prediction algorithm that emerged as the best after a thorough evaluation of some relevant classification algorithms is the C5.0 decision tree algorithm which had its percentage of correctly classified instances given as 78.4534%; the C5.0 algorithm searches the knowledge base recursively and matches the patient information with the pertinent rules that suits each case and thereafter gives the correct prediction as to whether the patient in question is susceptible to diabetes mellitus or not; it also provides a measure of the likelihood of the patient developing diabetes . The problem of accurately determining if a patient is prone to diabetes mellitus or not which has been a perennial problem in the domain of medicine will be most likely effectively combated with the solution provided by this framework.