International Journal of Engineering and Technology

Artificial neural networks are widely used in medical diagnosis replacing most of the conventional diagnosis methods due to its accuracy and speed. This paper analyses the variation in the accuracy of diagnosis of type II diabetes using Artificial Neural Networks based on the accuracy of the inputs given to the network. It compares the efficiency of the network based on the input format. The data needed for this comparison is collected by interviewing patients who approach the diabetician with various symptoms of the disease. These symptoms can be modeled in 2 different forms. One form just specifies the presence or absence of the symptom and can be represented using Boolean values. The other form specifies the severity or frequency of occurrence of the symptom. Both these inputs are given to the system and the accuracy of the output is analyzed. This result indicates the impact of the specification of the input on the output. Comparison is done by performing regression analysis on both the outputs. Regression analysis gives the correlation between the output of the system and the target [1]. It makes use of only the most general symptoms of the disease. Further analysis can be done on other diabetes particular symptoms.