Designing a Hybrid Genetic Algorithm Trained Feedforward Neural Network for Mental Health Disorder Detection

Abstract

This research delves into the innovative application of feed-forward neural networks (FNNs) specifically the multi-layer perceptron (MLP). MLP is a flexible algorithm due to its ability to adapt to different real-world problems amongst other features, and this makes it a preferred machine learning algorithm in the early detection of mental health disorders. MLP’s number of layers and the number of neurons per layer changes to accommodate these abilities. MLP was chosen for this work because they can model non-linear relationships found in a dataset as well as the fact that the algorithm is efficient in accuracy detection which is lacking in other types of FNN. This study focused on developing and optimizing MLP architectures to achieve heightened accuracy in identifying mental health disorders. Original dataset that was used comprise 334 rows (datapoints) and 31 columns (features) and Only 27 features were quantifiable. We utilized the first 13 features in the dataset for this research work as too many features will affect the training time. The model performance was evaluated using Accuracy, Precision, F1 Score, Recall, and the results of the model evaluation showed that early detection of mental health disorders is predictable using this type of Feed-forward Neural network, with an Accuracy of 96%, Recall of 80%, F1 Score of 77%, Sensitivity 90%, and Specificity of 88% when compared to previous research with lower accuracy of 81.75% amongst the other result they got for the other parameters. Furthermore, by using widely collected datasets and employing advanced machine learning techniques such as feature importance technique for optimization of the initial result got, this approach contributes significantly to the field of early detection of mental health disorders. Keywords: Mental Health Disorder, Feedforward Neural Network, Machine Learning, Early Detection, Artificial Intelligence