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A web-based clinical decision support system for the management of diabetes neuropathy using Nave bayes algorithm
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Abstract The use of Artificial Intelligence in medical diagnosis is becoming increasingly common and has been used widely in the diagnosis of cancers, tumors, diabetes, hepatitis, lung diseases, etc. There is a growing interest in the use of computer-based Clinical Decision Support Systems (CDSSs) to reduce medical errors and to increase health care quality and efficiency. Diabetes Neuropathy is a chronic health problem with devastating, yet preventable consequences. Due to this shortage of specialists, there is a need for a Clinical Decision Support System that will diagnose and manage diabetes neuropathy. This work therefore aimed at designing a web-based Clinical Decision Support System for the management of early diabetes neuropathy. Four pattern classification algorithms (K-nearest neighbor, Decision Tree, Decision Stump, and Rule Induction) were adopted in this study and were evaluated to choose the most precise algorithm to be employed in the developed clinical decision support system. The evaluation was carried out an appropriate dataset that was obtained from Babcock University Teaching Hospital and the University of Port Harcourt Teaching Hospital. The following benchmarks were used in comparing the generated models: performance, accuracy level, precision, confusion matrices, and the model building’s speed.