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All Journal International Journal of Artificial Intelligence International Journal of Advances in Artificial Intelligence and Machine Learning
Afuadajo, Emmanuel
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Computer Science & IT Decision Sciences, Operations Research & Management

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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): International Journal of Advances in Artificial Intelligence and Machine Learni
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/ijaaiml.v2i3.469

Abstract

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.
Design and Evaluation of a Fuzzy Logic Based Intrusion Detection System for Network Security Isijola, Ayomitope; Afuadajo, Emmanuel; Asefon, Michael; Ogude, Ufuoma; Akande, Jamiu; Joseph, Promise
International Journal of Artificial Intelligence Vol 12 No 2: December 2025
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijai-01202.870

Abstract

With the proliferation of networked systems, intrusion detection systems (IDS) have become vital in identifying and mitigating cyber threats and unauthorized access. Traditional IDS approaches, such as signature-based and anomaly-based methods, often struggle to detect novel attacks and tend to generate high false alarm rates. This study presents a robust, fuzzy logic-based IDS designed to detect network intrusions and assess their risk levels while minimizing false positives. The IDS classifies network intrusions by analyzing parameters such as source bytes, destination bytes, and packet rates, categorizing them into risk levels through defined fuzzy rules. Implemented in Python using libraries like scikit-fuzzy and pandas, the system utilizes the KDD Cup 99 dataset, a widely recognized IDS benchmark. Fuzzy membership functions and inference rules were defined for the primary input variables, enabling the system to infer intrusion likelihood. The IDS was tested using both two-variable and multi-variable input setups. It achieved a precision of 0.89, a recall of 0.85, and an F1-score of 0.87 in the multi-variable scenario. Results indicate that the fuzzy logic-based IDS achieves a balanced trade-off between detection accuracy and interpretability. It offers a transparent decision-making framework suitable for real-time applications due to its adaptability and potential for integration with live data streams. This research proposes future improvements by creating a foundation for hybrid intrusion detection systems (IDS) that integrate fuzzy logic and machine learning to enhance accuracy and interpretability. It recommends future research on adaptive fuzzy rules, real-time data processing, and explainable AI (XAI) to improve system flexibility, responsiveness, and transparency in cybersecurity applications.
Co-Authors Aifuobhokhan, Joy Akande, Jamiu Asefon, Michael Bowale, Deborah Adetola Ekechi, Chijioke Cyriacus Hussain, Ahmad Khalid Inioluwa, Oluwadare Marvellous Isijola, Ayomitope Joseph, Promise Ogude, Ufuoma Olanrewaju, Aisha Olasunbo