Medical disease prediction using Artificial Neural Networks

Abstract

This study examines a variety of artificial neural network (ANN) models in terms of their classification efficiency in an orthopedic disease, namely osteoporosis. Osteoporosis risk prediction may be viewed as a pattern classification problem, based on a set of clinical parameters. Multilayer perceptrons (MLPs) and probabilistic neural networks (PNNs) were used in order to face the osteoporosis risk factor prediction. This approach is the first computational intelligence technique based on ANNs for osteoporosis risk study on Greek population. MLPs and PNNs are both feed-forward networks; however, their modus operandi is different. Various MPL architectures were examined after modifying the number of nodes in the hidden layer, the transfer functions and the learning algorithms. Moreover, PNNs were implemented with spread values ranging from 0.1 to 50, and 4 or 2 neurons in output layer, according to coding of osteoporosis desired outcome. The obtained results lead to the conclusion that the PNNs outperform to MLPs, thus they are proved as appropriate computation intelligence technique for osteoporosis risk factor prediction. Furthermore, the overfitting problem was more frequent to MLPs, contrary to PNNs as their spread value increased. The aim of proposed PNN is to assist specialists in osteoporosis prediction, avoiding unnecessary further testing with bone densitometry.