A Multi-layer Feed Forward Neural Network Approach for Diagnosing Diabetes

Abstract

Diabetes is one of the worlds major health problems according to the World Health Organization. Recent surveys indicate that there is an increase in the number of diabetic patients resulting in an increase in serious complications such as heart attacks and deaths. Early diagnosis of diabetes, particularly of type 2 diabetes, is critical since it is vital for patients to get insulin treatments. However, diagnoses could be difficult especially in areas with few medical doctors. It is, therefore, a need for practical methods for the public for early detection and prevention with minimal intervention from medical professionals. A promising method for automated diagnosis is the use of artificial intelligence and in particular artificial neural networks. This paper presents an application of Multi-Layer Feed Forward Neural Networks (MLFNN) in diagnosing diabetes on publicly available Pima Indian Diabetes (PID) data set. A series of experiments are conducted on this data set with variation in learning algorithms, activation units, techniques to handle missing data and their impact on diagnosis accuracy is discussed. Finally, the results are compared with other states of art methods reported in the literature review. The achieved accuracy is 82.5% best of all related studies.