i-manager Publications

Enhanced Mental Health Prediction with Deep Neural Networks for Accurate Diagnosis

Takkellapati Ananya Madhuri*, Valamala Mounika**, Kattepogu Archana***, Saida Rao****, Chintalapudi V. Suresh*****

*-***** Vasireddy Venkatadri Institute of Technology, Guntur, Andhra Pradesh, India.

Periodicity:July - December'2024
DOI : https://doi.org/10.26634/jaim.2.2.20875

Abstract

This research investigates machine learning models for predicting mental health consequences using survey data. The study employs a two-phase approach first, it utilizes TensorFlow for initial Deep Neural Network (DNN) model building, and then it applies Random Forest (RF), Naive Bayes classifier, and decision tree methods for comparative analysis. The DNN model demonstrates strong performance, achieving high accuracy in mental health prediction. Metrics such as testing time, precision, mean absolute error, and accuracy are compared to provide insight into the advantages and disadvantages of each model. While the DNN model excels in accuracy and precision, other models offer trade-offs in computational efficiency. The results clarify the role of machine learning in mental wellness evaluation and intervention, providing guidance for further research and real-world applications. This research enhances the discourse on predictive modeling for mental health outcomes, facilitating advancements in leveraging machine learning to improve mental health assessment and intervention strategies.

Keywords

Machine Learning, Mental Health Prediction, Survey Data Analysis, Comparative Analysis, Deep Neural Network (DNN), Tensor Flow, Random Forest, Naive Bayes, Decision Tree, Testing Accuracy, Mean Absolute Error, Precision, Computational Efficiency, Intervention Strategies.

How to Cite this Article?

Madhuri, T. A., Mounika, V., Archana, K., Rao, S., and Suresh, C. V. (2024). Enhanced Mental Health Prediction with Deep Neural Networks for Accurate Diagnosis. i-manager’s Journal on Artificial Intelligence & Machine Learning, 2(2), 49-62. https://doi.org/10.26634/jaim.2.2.20875

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