A Comparative Study of Machine Learning Techniques for Early Prediction of Diabetes

Alzboon, Mowafaq Salem; Al-Batah, Mohammad; Alqaraleh, Muhyeeddin; Abuashour, Ahmad; Bader, Ahmad Fuad

doi:10.1109/comnet60156.2023.10366688

2023 IEEE Tenth International Conference on Communications and Networking (ComNet) 2023

DOI: 10.1109/comnet60156.2023.10366688

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A Comparative Study of Machine Learning Techniques for Early Prediction of Diabetes

Mowafaq Salem Alzboon,

Mohammad Al-Batah,

Muhyeeddin Alqaraleh

et al.

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Cited by 5 publications

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AI in the Sky: Developing Real-Time UAV Recognition Systems to Enhance Military Security

Mowafaq,

Muhyeeddin,

Al-Batah

2024

Data and Metadata

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In an era where Unmanned Aerial Vehicles (UAVs) have become crucial in military surveillance and operations, the need for real-time and accurate UAV recognition is increasingly critical. The widespread use of UAVs presents various security threats, requiring systems that can differentiate between UAVs and benign objects, such as birds. This study conducts a comparative analysis of advanced machine learning models to address the challenge of aerial classification in diverse environmental conditions without system redesign. Large datasets were used to train and validate models, including Neural Networks, Support Vector Machines, ensemble methods, and Random Forest Gradient Boosting Machines. These models were evaluated based on accuracy and computational efficiency, key factors for real-time application. The results indicate that Neural Networks provide the best performance, demonstrating high accuracy in distinguishing UAVs from birds. The findings emphasize that Neural Networks have significant potential to enhance operational security and improve the allocation of defense resources. Overall, this research highlights the effectiveness of machine learning in real-time UAV recognition and advocates for the integration of Neural Networks into military defense systems to strengthen decision-making and security operations. Regular updates to these models are recommended to keep pace with advancements in UAV technology, including more agile and stealthier designs

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AI in the Sky: Developing Real-Time UAV Recognition Systems to Enhance Military Security

Mowafaq,

Muhyeeddin,

Al-Batah

2024

Data and Metadata

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AI Rx: Revolutionizing Healthcare Through Intelligence, Innovation, and Ethics

Wahed,

Alqaraleh,

Alzboon

et al. 2025

Seminars in Medical Writing and Education

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The integration of artificial intelligence (AI) in healthcare presents significant promise to enhance clinical procedures and patient outcomes. This research examines the setting, methodology, conclusions, and issues associated with AI in healthcare. The swift proliferation of digital health data, encompassing medical imaging and clinical records, has generated substantial prospects for AI applications. Artificial intelligence methodologies, including machine learning, natural language processing, and computer vision, facilitate the derivation of significant insights from intricate datasets, hence improving clinical decision-making. A thorough literature review examines the practical applications of AI, encompassing its roles in medical diagnostics, treatment planning, and patient outcome prediction. The report also examines ethical issues, data protection, and legal frameworks, which are crucial for the responsible application of AI in healthcare. The results illustrate AI's capacity to enhance diagnostic precision, facilitate administrative efficiency, and optimise resource distribution, resulting in tailored therapies and improved healthcare administration. Nonetheless, obstacles persist, such as data integrity, algorithm transparency, and ethical considerations, which must be resolved to guarantee the secure and efficient deployment of AI. Continuous research, cooperation between healthcare and AI experts, and the establishment of comprehensive regulatory frameworks are essential for optimising the advantages of AI while minimising hazards. This research highlights AI's capacity to transform healthcare, stressing the necessity for a multidisciplinary strategy to effectively harness its benefits and tackle the associated ethical and regulatory dilemmas.

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Application of Artificial Intelligence for Diagnosing Tumors in the Female Reproductive System: A Systematic Review

Abdel Wahed,

Alqaraleh,

Salem Alzboon

et al. 2025

Multidisciplinar (Montevideo)

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The diagnosis of tumors in the female reproductive system is crucial for effective treatment and patient outcomes. The advent of artificial intelligence (AI) has introduced new possibilities for enhancing diagnostic accuracy and efficiency. A comprehensive search across PubMed, Scopus, and Web of Science for articles published from 2018 to 2023 on artificial intelligence (AI), machine learning (ML), deep learning (DL), and convolutional neural networks (CNN) in diagnosing cancers of the female reproductive system yielded 15,900 articles. After a rigorous screening process excluding conference proceedings, book chapters, reports, non-English publications, and duplicates, 98 unique peer-reviewed journal articles remained. These were further assessed for relevance and quality, resulting in the final inclusion of 29 high-quality articles. The review includes a summary of various AI methodologies used, their diagnostic accuracy, and comparative performance against traditional diagnostic methods. The findings indicate a significant improvement in diagnostic precision and efficiency when AI is employed. AI holds substantial promise for enhancing the diagnosis of tumors in the female reproductive system. Future research should focus on larger-scale studies and the integration of AI into clinical workflows to fully realize its potential

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A Comparative Study of Machine Learning Techniques for Early Prediction of Diabetes

Cited by 5 publications

References 20 publications

AI in the Sky: Developing Real-Time UAV Recognition Systems to Enhance Military Security

AI in the Sky: Developing Real-Time UAV Recognition Systems to Enhance Military Security

AI Rx: Revolutionizing Healthcare Through Intelligence, Innovation, and Ethics

Application of Artificial Intelligence for Diagnosing Tumors in the Female Reproductive System: A Systematic Review

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