Abstract
Nature presents an infinite source of inspiration for computational models and paradigms, in particular for researchers associated with the area known as natural computing. The simultaneous optimization of the architectures and weights of artificial neural networks (ANNs) through biologically inspired algorithms is an interesting approach for obtaining efficient networks with relatively good generalization capabilities. This methodology constitutes a concordance between a low structural complexity model and low training error rates. Currently, complexity and high error rates are the leading issues faced in the development of clinical decision support systems (CDSSs) for pregnancy care. Hence, in this paper the use of a biologically inspired technique, known as particle swarm optimization (PSO), is proposed for reducing the computational cost of the ANN-based method referred to as the multilayer perceptron (MLP), without reducing its precision rate. The results show that the PSO algorithm is able to improve computational model performance, showing lower validation error rates than the conventional approach. This technique can select the best parameters and provide an efficient solution for training the MLP algorithm. The proposed nature-inspired algorithm and its parameter adjustment method improve the performance and precision of CDSSs. This technique can be applied in electronic health (e-health) systems as a useful tool for handling uncertainty in the decision-making process related to high-risk pregnancy. The proposed method outperformed, on average, other approaches by 26.4% in terms of precision and 14.9% in terms of the true positive ratio (TPR), and showed a reduction of 35.4% in the false positive ratio (FPR). Furthermore, this method was superior to the MLP algorithm in terms of precision and area under the receiver operating characteristic curve by 2.3 and 10.2%, respectively, when applied to the delivery outcome for pregnant women.