Performance evaluation of Levenberg-Marquardt technique in error reduction for diabetes condition classification

This paper aims to provide a case study to classify diabetes medical condition amongst patients. The study examines the
performance of the Levenberg-Marquardt (LM) algorithm on a single dataset, the Pima Indian Diabetes dataset, attempting to
minimize error in classifying the patients as diabetes positive or negative. The learning algorithm is applied on dynamically
constructed neural network to minimize the error by continuously training the network until the optimum efficiency level is
obtained. The performance of the approach is verified by performing a comparison study. The comparison study involves
testing of the dynamically constructed network and presents a critical analysis of the classification output. The performance
of the network is measured in terms of sensitivity and specificity for different learning algorithms. The study reveals that the
LM algorithm outperforms other techniques in these tests and consequently concludes it to be the best ANN learning rule in
providing optimum output results when applied to a dynamically constructed neural network.

International Conference on Computer Science ICCS02013