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.
Enaodona, O , Akazue, M & Edje, A (2024). Designing a Hybrid Genetic Algorithm Trained Feedforward Neural Network for Mental Health Disorder Detection. Afribary. Retrieved from https://afribary.com/works/designing-a-hybrid-genetic-algorithm-trained-feedforward-neural-network-for-mental-health-disorder-detection
Enaodona, Oweimieotu et. al. "Designing a Hybrid Genetic Algorithm Trained Feedforward Neural Network for Mental Health Disorder Detection" Afribary. Afribary, 03 Apr. 2024, https://afribary.com/works/designing-a-hybrid-genetic-algorithm-trained-feedforward-neural-network-for-mental-health-disorder-detection. Accessed 26 Dec. 2024.
Enaodona, Oweimieotu, Maureen Akazue and Abel Edje . "Designing a Hybrid Genetic Algorithm Trained Feedforward Neural Network for Mental Health Disorder Detection". Afribary, Afribary, 03 Apr. 2024. Web. 26 Dec. 2024. < https://afribary.com/works/designing-a-hybrid-genetic-algorithm-trained-feedforward-neural-network-for-mental-health-disorder-detection >.
Enaodona, Oweimieotu , Akazue, Maureen and Edje, Abel . "Designing a Hybrid Genetic Algorithm Trained Feedforward Neural Network for Mental Health Disorder Detection" Afribary (2024). Accessed December 26, 2024. https://afribary.com/works/designing-a-hybrid-genetic-algorithm-trained-feedforward-neural-network-for-mental-health-disorder-detection