Predicting and Curing Depression Using Long Short Term Memory and Global Vector
Computers, Materials and Continua, ISSN: 1546-2226, Vol: 74, Issue: 3, Page: 5837-5852
2023
- 3Citations
- 26Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
In today's world, there are many people suffering from mental health problems such as depression and anxiety. If these conditions are not identified and treated early, they can get worse quickly and have far-reaching negative effects. Unfortunately, many people suffering from these conditions, especially depression and hypertension, are unaware of their existence until the conditions become chronic. Thus, this paper proposes a novel approach using Bi-directional Long Short-Term Memory (Bi-LSTM) algorithm and Global Vector (GloVe) algorithm for the prediction and treatment of these conditions. Smartwatches and fitness bands can be equipped with these algorithms which can share data with a variety of IoT devices and smart systems to better understand and analyze the user's condition. We compared the accuracy and loss of the training dataset and the validation dataset of the two models namely, Bi-LSTM without a global vector layer and with a global vector layer. It was observed that the model of Bi-LSTM without a global vector layer had an accuracy of 83%,while Bi-LSTMwith a global vector layer had an accuracy of 86% with a precision of 86.4%, and an F1 score of 0.861. In addition to providing basic therapies for the treatment of identified cases, our model also helps prevent the deterioration of associated conditions, making our method a real-world solution.
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