# Intelligent Neonatal Sepsis Early Diagnosis System for Very Low Birth Weight Infants
## 3 Materials And Methods
### ANN Design  
In order to design the ANN topology, in this work, an evolutionary approach based on a single-objective genetic algorithm (GA) was employed for selecting the optimal combination in terms of the number of hidden layers, the number of neurons per layer and the activation function for each hidden layer, as in previous works of some of the authors, which was revealed to be a suitable approach for solving problems in several clinical domains [23; 24; 25]. The MATLAB(r) framework was employed for designing and implementing both the neural models and the optimisation algorithm. The GA searched for the optimal ANN topology by maximizing the mean accuracy (Equation (1)), measured on the test set, over 100 iterations of training, validation and testing of the ANN. To do this, each individual in the GA population, namely each can \begin{table}
\begin{tabular}{c c c} \hline
**Parameters** & **Threshold Values** & **Assigned Score** \\ \hline Birth Weight & \(\leq\)1000 g & 1 \\  & \(>\)1000 g & 0 \\ \hline Gestational Age & \(<\)26 weeks & 1 \\  & \(>\)26 and \(<\)30 weeks & 0.5 \\  & \(>\)30 weeks & 0 \\ \hline General Aspect & sick & 1 \\  & healthy & 0 \\ \hline Symptoms of Sepsis & yes & 1 \\  & no & 0 \\ \hline Positive Cultures & yes & 1 \\  & no & 0 \\ \hline CRP & \(>\)1 ug/dL & 1 \\  & \(\geq\)0.5 and \(\leq\)1 ug/dL & 0.5 \\  & \(<\)0.5 ug/dL & 0 \\ \hline PCT & \(>\)1 ug/dL & 1 \\  & \(\geq\)0.5 and \(\leq\)1 ug/dL & 0.5 \\  & \(<\)0.5 ug/dL & 0 \\ \hline Metabolic Acidosis & pH \(\leq\)7.25 & 1 \\  & pH \(>\)7.25 & 0 \\ \hline Day with CVC & \(>\)10 & 1 \\  & \(>\)7 and \(\leq\)10 & 0.5 \\  & \(\leq\)7 & 0 \\ \hline Intubated Patients & yes & 1 \\  & no & 0 \\ \hline HeRO Score & \(\geq\)2 & 1 \\  & \(>\)1 and \(<\)2 & 0.5 \\  & \(\leq\)1 & 0 \\ \hline \end{tabular}
\end{table} 
Table 2: ANN input parameters and thresholds used for data discretisation. CVC, central venous catheter. didate ANN topology, was trained, validated and tested 100 times considering random permutations of the input dataset, splitting it into 60-10-30% for training, validation and test sets, respectively. Each ANN could be constituted by at least one hidden layer, up to three, with at maximum 256 neurons per layer. The GA also evaluated several activation functions (each hidden layer shared the same transfer function for all the neurons of the layer), namely the hyperbolic tangent sigmoid transfer function (_tansig_), the log-sigmoid transfer function (_logsig_), the linear transfer function (_purelin_) and the symmetric saturating linear transfer function (_satlins_). The output layer was always characterised by a tansig transfer function, whereas the ANN training was performed with resilient propagation [26]. Although in [PERSON] et al., the 60-20-20 percentages were used for splitting the dataset [23], in this work, due to a limited number of samples, ten percent only was employed for the validation set, since it was used for evaluating the early-stopping of the network training in order to avoid over-fitting. Furthermore, since the input data were unbalanced between the positive and negative classes, the adaptive synthetic (ADASYN) approach was considered for generating synthetic data for the class with fewer samples [22]. In this way, the final dataset was constituted by 46 samples (24 negative and 22 positive), described by 11 features. Tests were performed on the topology-optimized ANN computing the average performance on 100 iterations of training, validation and testing.