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With advanced data analytical techniques, efforts for more accurate decisionsupport systems for disease prediction are on rise. Surveys by World HealthOrganization (WHO) indicate a great increase in number of diabetic patients andrelated deaths each year. Early diagnosis of diabetes is a major concern amongresearchers and practitioners. The paper presents an application of\textit{Automatic Multilayer Perceptron }which\textit{ }is combined with anoutlier detection method \textit{Enhanced Class Outlier Detection usingdistance based algorithm }to create a prediction framework named as EnhancedClass Outlier with Automatic Multi layer Perceptron (ECO-AMLP). A series ofexperiments are performed on publicly available Pima Indian Diabetes Dataset tocompare ECO-AMLP with other individual classifiers as well as ensemble basedmethods. The outlier technique used in our framework gave better results ascompared to other pre-processing and classification techniques. Finally, theresults are compared with other state-of-the-art methods reported in literaturefor diabetes prediction on PIDD and achieved accuracy of 88.7\% bests all otherreported studies.

ECO-AMLP: A Decision Support System using an Enhanced Class Outlier with Automatic Multilayer Perceptron for Diabetes Prediction