This project is an innovative mental health diagnosis tool that leverages the power of Natural Language Processing (NLP) and Machine Learning (ML). It is designed to understand and analyze inputs in the Arabic language, making it one of the few of its kind.
The core of this project lies in its use of PyTorch, Transformers, and AraBERT. PyTorch, an open-source machine learning library, provides the tensor computation and deep neural networks needed for this project. Transformers, a state-of-the-art Natural Language Processing library, offers numerous pre-trained models which are used to understand the semantics and context of the Arabic language. AraBERT, a version of BERT specifically trained for Arabic, is used to further enhance the understanding of the Arabic language inputs.
The goal of this project is to provide a potential mental disorder diagnosis based on a person's lifestyle and daily life inputs. The user can input any thoughts or feelings they have, and the tool will analyze these inputs and provide a potential diagnosis. This diagnosis is not meant to replace professional medical advice, but rather to provide an initial analysis that can help guide users to seek professional help if necessary.
This tool is unique in its approach to mental health diagnosis. By using NLP and ML, it can provide a quick and anonymous initial diagnosis, making mental health diagnosis more accessible to people who might otherwise avoid it due to stigma or other reasons.
The primary objective of this project is to develop an accessible and efficient mental health diagnostic tool. This tool uses advanced Natural Language Processing and Machine Learning techniques to analyze Arabic language inputs related to a user's lifestyle and daily thoughts.
Based on this analysis, the tool provides a potential mental disorder diagnosis. This diagnosis can serve as an initial step in mental health care, encouraging users to seek professional help if necessary.
In addition, this project aims to reduce the stigma associated with mental health by providing an anonymous and private platform for users to express their thoughts and feelings.
Before you begin, ensure you have met the following requirements:
- You have installed the latest version of Python, PyTorch, Transformers, and Laravel.
- You have a
<Windows/Linux/Mac>machine. State which OS is supported/required. - You have read
<guide/link/documentation_related_to_project>.
- Clone the repo and cd into it
- In your terminal composer install
- Rename or copy .env.example file to .env
- php artisan key:generate
- Set your database credentials in your .env file
- npm install
- npm run watch
- run command[laravel file manager]:- php artisan storage:link -run php artisan serve
The project involved several steps to ensure the model was trained effectively and accurately. Here's a summary of the work done:
The dataset was meticulously assembled from a diverse range of sources and websites. This comprehensive collection process ensured a rich variety of data, enhancing the robustness of our model. To cater to our Arabic-speaking audience, the data was then translated into Arabic.
To uphold the highest standards of accuracy, the translated dataset underwent a rigorous validation process. Medical professionals, with their extensive knowledge and expertise, meticulously reviewed the data. Their approval served as a testament to the validity and reliability of our dataset.
The dataset consisted of 40,000 rows with two columns. One column contained the user's story and daily life, averaging around 500 words each. The other column contained the disease name, which was mapped to one of four numbers. The data was preprocessed to be usable for the model.
The model was trained using AraBERT and Transformers. Some layers were frozen during the training process to prevent them from being updated. The maxlen parameter was set to 512 to accommodate the tokenization of cells containing around 500 words each.
To improve the model's performance, a dropout layer and batch normalization were added. These additions helped to prevent overfitting and ensure that the model generalizes well to new data.
After weeks of training, a weights file of 500 megabytes was obtained. This file is used to correctly predict the disease based on the user's input.
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- Vehikl
- Tighten Co.
- Kirschbaum Development Group
- 64 Robots
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- Many
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- DevSquad
- Curotec
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- Lendio
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This project would not have been possible without the help and support of several individuals. I would like to express my deepest appreciation to:
- https://github.com/HadoolMohamma : My project partner who worked alongside me on the Natural Language Processing section of this project. You can find more about their work here.
- assfourali98@gmail.com: My project partner who worked on the web development section of this project.