We used a cost-sensitive multilayer perceptron (CSMLP) for our study because of the notable class imbalance in our dataset. It is assumed that the costs of misclassification (false positives and false negatives) are equivalent for conventional multilayer perceptrons (MLPs) trained using the backpropagation of error algorithm. To overcome this difficulty, we used the CSMLP since a false negative is more expensive than a false positive . To better account for class imbalance, we adjusted the loss function of the MLP in our suggested CSMLP. In particular, we increased the penalty for errors on minority-class samples by using focal loss . Each sample is given a weight by focal loss according to its difficulty, which is determined by the loss that the CSMLP experiences on that sample. Higher loss samples are seen as more difficult. The following is the focal loss (FL) equation: