International Journal of Advances in Artificial Intelligence and Machine Learning
The International Journal of Advances in Artificial Intelligence and Machine Learning (IJAAIML) is a prominent academic journal dedicated to publishing cutting-edge research and developments in the fields of Artificial Intelligence (AI) and Machine Learning (ML). It serves as an essential platform for researchers, practitioners, and professionals worldwide to share innovative ideas, technologies, and empirical studies that contribute to advancing AI and ML. The journal emphasizes both theoretical advancements and practical applications, showcasing how these technologies are shaping various industries, including healthcare, finance, education, robotics, and autonomous systems. IJAAIML covers a wide range of topics within AI and ML, such as deep learning, neural networks, natural language processing (NLP), computer vision, robotics, data mining, reinforcement learning, and AI ethics. The journal is open to diverse types of scholarly contributions, including original research articles, review papers, case studies, technical notes, and surveys. It encourages submissions that introduce novel algorithms, methodologies, and systems, as well as those addressing challenges and proposing new approaches in AI and ML. This broad scope allows the journal to remain at the forefront of technological innovation, providing valuable insights into the latest trends and developments in the field. The journal maintains high academic standards through a rigorous peer-review process, ensuring that each published article is of exceptional quality and originality. Submissions are evaluated by experts in relevant fields based on their significance, innovation, methodology, and clarity. This commitment to quality makes IJAAIML a trusted source of information for a diverse audience, including academic researchers, industry professionals, AI practitioners, and students who seek to stay informed about the latest advances in AI and ML. IJAAIML is committed to global knowledge dissemination, making its publications accessible to researchers and professionals worldwide through its online platform. This approach fosters knowledge exchange and collaboration across borders, enabling the journal to reach a broad international audience. By highlighting state-of-the-art research that addresses real-world problems using AI and ML technologies, IJAAIML plays a significant role in advancing the understanding and application of these technologies. Additionally, the journal explores the ethical, societal, and economic impacts of AI and ML, promoting discussions on responsible AI practices and future directions. By contributing to these conversations, IJAAIML not only advances technological innovation but also encourages the development of AI and ML in a manner that considers broader implications for society. Overall, the International Journal of Advances in Artificial Intelligence and Machine Learning stands as a crucial resource for anyone involved in AI and ML, driving forward the frontiers of these dynamic fields through high-quality, peer-reviewed research.
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21 Documents
Predicting Thyroid Cancer Recurrence Using Machine Learning: An Artificial Intelligence Approach to Clinical Oncology
Aifuobhokhan, Joy;
Hussain, Ahmad Khalid;
Ekechi, Chijioke Cyriacus;
Olanrewaju, Aisha Olasunbo;
Afuadajo, Emmanuel;
Bowale, Deborah Adetola;
Inioluwa, Oluwadare Marvellous
International Journal of Advances in Artificial Intelligence and Machine Learning Vol. 2 No. 3 (2025): Forthcoming Issue
Publisher : CV Media Inti Teknologi
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DOI: 10.58723/ijaaiml.v2i3.469
Background of study: Differentiated thyroid cancer (DTC) accounts for most thyroid malignancies and has favorable survival outcomes, yet up to 30% of patients experience recurrence, placing strain on follow-up systems in resource-limited settings. Conventional staging tools offer limited predictive precision. With increasing interest in machine learning (ML) for precision oncology, there is a need for interpretable, deployable models suitable for low-resource environments.Aims and scope of paper: To develop and validate an interpretable machine learning model for predicting thyroid cancer recurrence and assess its feasibility for deployment in constrained clinical settings, including African oncology contexts.Methods: A retrospective dataset of 383 DTC patients with at least 10-year follow-up was sourced from the UCI Machine Learning Repository. Thirteen demographic, clinical, and treatment-related predictors were included. Data preprocessing involved encoding, scaling, and class balancing using SMOTE. Logistic Regression, Random Forest, K-Nearest Neighbors, and Extreme Gradient Boosting (XGBoost) were trained with hyperparameter tuning via grid search and cross-validation. Performance was evaluated using accuracy, precision, recall, F1 score, and AUC-ROC.Result: XGBoost achieved the best performance with 97% accuracy, 95% recall, 94% precision, and an AUC-ROC of 0.93. The most influential predictors were age, smoking status, T and M staging, ATA risk category, and adenopathy. The final model was deployed as a browser-based decision support tool to enable real-time recurrence risk estimation.Conclusion: This study presents a high-performing and interpretable ML model for predicting DTC recurrence, demonstrating feasibility for use in low-resource oncology settings. External validation with African clinical datasets and integration into electronic health systems is recommended to enhance equity and clinical uptake.