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

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 realworld 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 nonlinear 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.