An expert system for diabetes prediction using auto tuned multi-layer perceptron

Medical Expert Systems is an active research area where data analysts and medical experts are continuously collaborating to make these systems more accurate and therefore, more useful in real life. Recent surveys by World Health Organization indicated a great increase in number of diabetic patients and the deaths that are attributed to diabetes each year. Therefore, early diagnosis of diabetes is a major concern among researchers and practitioners. The paper presents an application of automatic multilayer perceptron (AutoMLP) which is combined with an outlier detection method Enhanced Class Outlier Detection using distance based algorithm to create a novel prediction framework. AutoMLP is an auto-tunable and performs parameter optimization automatically on the run during training process, which otherwise requires human intervention. Our framework performs outlier detection during pre-processing of data. A series of experiments are performed publicly available dataset: UCI (Prima Indian) and system achieved an accuracy of 88.7% which bests the highest reported results.