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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Ayanouz, Soufyane
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Computer Science & IT Engineering

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Using natural language processing to evaluate the impact of specialized transformers models on medical domain tasks Ayanouz, Soufyane; Anouar Abdelhakim, Boudhir; Ben Ahmed, Mohammed
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1732-1740

Abstract

We are presently living in the age of intelligent machines, machines are rapidly imitating humans as a result of technological breakthroughs and advances in machine learning, deep learning, and artificial intelligence. In our work, we based our approach on the idea of utilizing a specialized corpus to enhance the performance of a pre-trained language model. We utilized the following approach: (V = vocabulary domain, C1 = initial corpus, C2 = specialization corpus). We applied this approach with different combinations such as (V = general, C1 = general, C2 = ∅), (V = general, C1 = general, C2 = medical), (V = medical, C1 = medical, C2 = ∅), and (V = medical, C1 = medical, C2 = medical) to compare the performance of a general bidirectional encoder representations from transformers model and specialized BERT models for the medical domain. In addition, we evaluated the model’s using informatics for integrating biology and the bedside, and drug-drug interaction datasets to measure their effectiveness in medical tasks.
Machine learning algorithms for breast cancer analysis: performance and accuracy comparison Ayanouz, Soufyane; Anouar Abdelhakim, Boudhir; Ben Ahmed, Mohammed
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4372-4379

Abstract

Breast cancer, a leading cause of cancer mortality among women, necessitates early detection to improve survival rates. Traditional diagnostics face accuracy and speed limitations, prompting this study to explore machine learning for enhanced diagnostics. We applied bidirectional encoder representations from transformers (BERT), long short-term memory (LSTM), naive Bayes, support vector machine (SVM), and random forest to the Breast Cancer Wisconsin dataset, implementing a thorough methodology involving data preprocessing, feature extraction, and model validation. BERT led in accuracy at 92.5%, showcasing advanced algorithms' potential in medical diagnostics, with random forest 90.6%, SVM 89.3%, LSTM 88.7%, and naive Bayes 85.2%; also showing promising results. The study underscores the importance of incorporating machine learning, especially BERT, into clinical decision-making, potentially revolutionizing breast cancer diagnostics by improving accuracy and efficiency. We recommend healthcare practitioners integrate these algorithms into their diagnostic processes. Future research should reeefine these algorithms and extend their application to enhance patient care further.
Co-Authors Anouar Abdelhakim, Boudhir Ben Ahmed, Mohammed