Development of a decision support system tool to predict the pulmonary function using artificial neural network approach

Abstract

The spirometry is considered a preclinical tool for the evaluation of the respiratory system. The formal lung volumes measurement and health status lung system are made using spirometry. Artificial neural network (ANN) has been introduced in solving complex problems in a large number of different settings, including medical diagnosis support system as predictive power. An objective of this research was intended to investigate the development of a new decision support system (DSS) using ANN modeling approaches and algorithms to predict pulmonary function in people. The spirometry data and general characteristics, anthropometric data, and body composition parameters (N = 130) were obtained from subjects. The classification of pulmonary function was performed by the multi-layer perceptron (MLP) model. Findings show that the MLP model is capable of classifying respiratory abnormalities in different people. The ANN model was totally 93.6%, 92.3%, 84.6%, and 91.5% successful in correctly classified in training, validation, test, and all data, respectively. Also, a DSS tool was created that allows the evaluation and classification of the results of spirometry data. It appears that ANNs are useful in classification pulmonary function.

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