A Machine Learning Framework for Enhanced Depression Detection in Mental Health Care Setting

Authors

  • Suhag Pandya  Independent Researcher, India

DOI:

https://doi.org/10.32628/IJSRSET2358715

Keywords:

Mental Health, Depression, Depression Detection, Machine Learning, Diagnosis, Healthcare Technology, DAIC-WOZ Dataset.

Abstract

One of the most overlooked yet vital aspects of our general well-being in the modern day is mental health. There are many persons who experience various mental health conditions and diseases. The present investigation describes an improved model for diagnosing depression in clinical and healthcare environments with the help of the DAIC-WOZ Depression Database. The data set used in this work comprises 80% training data and 20% testing data; criteria like classification evaluation, such as accuracy, precision, recall and the F-index measures, are applied here to evaluate the efficiency of the selected machine learning models, including KNN, CNN, MTL and XGBoost. Based on the archival findings, it is clear that algorithm performance of the XGBoost model is superior to other models positively categorised pictures with an accuracy of 97.02%; precision of 97.03%, recall of 97.01 % and F1-score of 97.02%. A comparison between the outcomes of Multi-Task Learning (MTL), More specifically, it will draw from Convolutional Neural Networks (CNN), and K-Nearest Neighbours (KNN). Classification models illustrate that XGBoost has a superior performance across all measurements for offering accurate depression detection in clinical practice. One more is the use of more extensive multiple modalities, e.g., physiological data or social media, to enhance the rate of depression identification. Transformers, as well as the integration of deep learning hybrid networks, may be used to capture data dependencies in the dataset.

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International Journal of Scientific Research in Science, Engineering and Technology

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Published

2023-10-30

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Volume 10, Issue 5, September-October-2023

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Research Articles

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[1]
Suhag Pandya "A Machine Learning Framework for Enhanced Depression Detection in Mental Health Care Setting" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 10, Issue 5, pp.356-368, September-October-2023. Available at doi : https://doi.org/10.32628/IJSRSET2358715