Parallel versus cascaded logistic regression trained single-hidden feedforward neural network for medical data

Abstract

ObjectiveAn important step towards a better healthcare system is fast and accurate diagnosis. In the last decade, the application of intelligent systems in healthcare has led to impressive results. The goal of this paper is to extend the LogSLFN (single-hidden layer feedforward neural network trained using logistic regression) algorithm, which has been deployed successfully in the past for the case of a two-class decision problem, to the case of multiple classes. We have considered and statistically analyzed two approaches: a parallel LogSLFN, and a cascaded LogSLFN. Materials and methodsAccording to the universal approximation theorem, a single-hidden layer feedforward neural network has the ability to approximate arbitrarily closely continuous functions of several real variables under certain reasonable assumptions. Essentially, a single hidden layer containing a finite fixed number of neurons is sufficient to provide an arbitrarily well approximation to a given training set of inputs and a desired target output represented by a continuous function. Parallel LogSLFN and cascaded LogSLFN are two novel approaches that can be applied to multiple-class decision problems. Both methods are extensions of the LogSLFN, which uses logistic regression to compute the weights between the input and hidden layer of a single-hidden layer feedforward network. No error correction is needed, the weights between the hidden and the output layer being computed using the Moore-Penrose pseudoinverse matrix. The proposed models have been tested on two medical datasets regarding cancer diagnosis and liver fibrosis staging. Experimental results and the subsequent statistical analysis have proved the robustness of the proposed models with other machine learning techniques reported in literature. Main findingsThe experimental results showed that the Parallel approach surpasses the Cascaded one. Still, both models are competitive to the other state-of-the-art techniques. ConclusionsThe LogSFLN algorithm can be successfully extended to multiple-class decision problems. By embedding knowledge extracted from the data into the architecture, we obtained a raise by 20% in accuracy when applied on the liver fibrosis dataset.