Mai A. Shaaban, Mariam Kashkash, Maryam Alghfeli, Adham Ibrahim
Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE
One of the challenges that artificial intelligence engineers face, specifically in the field of deep learning is obtaining the optimal model hyperparameters. The search for optimal hyperparameters usually hinders the progress of solutions to real-world problems such as healthcare. To overcome this hurdle, the proposed work introduces a novel mechanism called "OptBA" to automatically fine-tune the hyperparameters of deep learning models by leveraging the Bees Algorithm, which is a recent promising swarm intelligence algorithm. In this paper, the optimization problem of OptBA is to maximize the accuracy in classifying ailments using medical text, where initial hyperparameters are iteratively adjusted by specific criteria. Experimental results demonstrate a noteworthy enhancement in accuracy with approximately 1.4%. This outcome highlights the effectiveness of the proposed mechanism in addressing the critical issue of hyperparameter optimization and its potential impact on advancing solutions for healthcare and other societal challenges.
Before you begin, ensure you have met the requirements by running the following command: pip install -r requirements.txt
Data files are included in the data folder.
Link: https://www.kaggle.com/datasets/paultimothymooney/medical-speech-transcription-and-intent
To reproduce results, just run the following command: python BA.py
- To apply different LSTM structures or other deep learning models, use
lstm_module.py - For text/data preprocessing, use
preprocessing_module.py
This project uses Apache License Version 2.0.