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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Journal of Applied Data Sciences
Abdelhafez, Hoda A.
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering

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Machine Learning Techniques for Diabetes Prediction: A Comparative Analysis Abdelhafez, Hoda A.; Amer, Abeer A.
Journal of Applied Data Sciences Vol 5, No 2: MAY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i2.219

Abstract

Diabetes mellitus, characterized by chronic hyperglycemia, presents significant challenges due to its associated complications and increasing morbidity rates. This study examines a range of machine learning algorithms such as Naìˆve Bayes, Decision Tree, Logistic Regression, Random Forest, Neural Network, Support Vector Machine, LogitBoost, and Voting classifier to develop accurate predictive models for diabetes. The data used in this research is drawn from a comprehensive dataset available on mendeley.com, sourced from the laboratory of Medical City Hospital in Iraq. The focus of the study is on feature selection and evaluation metrics to effectively gauge model performance. Eight classification techniques are employed and compared, including Decision Trees (DT), Random Forests (RF), and LogitBoost. The study's findings highlight DT and RF as the top-performing algorithms, demonstrating comparable predictive abilities, with LogitBoost also showing promising results. Conversely, Support Vector Machine (SVM) shows reduced performance due to its sensitivity to outliers. These insights enable healthcare practitioners to adopt appropriate machine learning methods to improve diabetes prediction, thus enabling timely interventions and enhancing patient outcomes.
Arabic text classification using machine learning and deep learning algorithms Alqahtani, Rawad Awad; Abdelhafez, Hoda A.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp5201-5217

Abstract

The classification of Arabic textual content presents considerable challenges due to the language's rich morphological structure and the wide variation among its dialects. This study aims to enhance classification accuracy by leveraging ensemble learning techniques and a deep bidirectional transformer-based model, specifically the multilingual autoregressive BERT (MARBERT). To address linguistic variability, advanced preprocessing techniques were employed, including Farasa, Tashaphyne, and Assem stemming methods. The Al Khaleej dataset served as the basis for supervised learning, providing a representative sample of Arabic text. Furthermore, term frequency-inverse document frequency (TF-IDF) with bigram and trigram feature extraction was utilized to effectively capture contextual semantics. Experimental results indicate that the proposed approach, particularly with the integration of MARBERT, achieves a peak classification accuracy of 98.59%, outperforming existing models. This research underscores the efficacy of combining ensemble learning with deep transformer-based models for Arabic text classification and highlights the critical role of robust preprocessing techniques in managing linguistic complexity and improving model performance.
Co-Authors Alqahtani, Rawad Awad Amer, Abeer A.