Category: Education

On October 21, 2026, MSc candidate Sanja Lazić successfully defended her thesis entitled “Interpretable Machine Learning and Deep Learning Models for Type 2 Diabetes Risk Prediction Based on Behavioural and Clinical Indicators” within the Artificial Intelligence Master’s programme at the University of Donja Gorica, Faculty for Information Systems and Technologies, under the mentorship of Prof. Dr. Tomo Popović.

The research and experimental work were partially supported by NCC Montenegro through HPC infrastructure and expertise within the EuroCC3 project, with additional research support from the AI-AGE project. Through its involvement in academic research, mentoring, and competence development, NCC Montenegro contributes to strengthening the application of advanced AI and HPC technologies in healthcare and other scientific domains.

The thesis investigates the application of machine learning, deep learning, and explainable AI techniques to type 2 diabetes risk assessment, focusing on predictive performance, reliability, and interpretability. The research also demonstrates how AI models can support preventive healthcare through accessible, questionnaire-based risk assessment without requiring laboratory measurements.

ABSTRACT – This thesis presents the development and systematic evaluation of machine learning and deep learning models for type 2 diabetes risk assessment using behavioural, clinical, and demographic indicators. The study analysed 253,680 records and compared five models: Logistic Regression, Random Forest, XGBoost, LightGBM, and a deep neural network. Model performance was evaluated in terms of discrimination, probability calibration, and interpretability. While the deep neural network achieved the highest ROC-AUC (0.7728), LightGBM demonstrated the most balanced overall performance. Explainable AI methods, including TreeSHAP, Integrated Gradients, and DeepLIFT SHAP, were applied to identify factors contributing to model predictions. A prototype web application was developed to demonstrate how estimated risks and their explanations could be presented to users.

The results demonstrate the potential of explainable AI for supporting non-invasive risk assessment and preventive screening, while highlighting the importance of model reliability, transparency, and further validation before clinical application.

On June 29, 2026, MSc candidate Nikola Kavarić successfully defended his thesis entitled “Synergy of Computer Vision and Natural Language Processing in Tuberculosis Diagnostics and Education” within the Artificial Intelligence Master’s programme at the University of Donja Gorica. Through its support for the programme, mentoring activities, and development of competencies in artificial intelligence and high-performance computing, NCC Montenegro contributes to preparing young researchers to develop interdisciplinary AI solutions for healthcare. The thesis investigates the combination of computer vision and Retrieval-Augmented Generation approaches for detecting signs of tuberculosis and providing educational explanations of medical findings.

ABSTRACT – The aim of this thesis is the development and evaluation of a system that combines computer vision and Retrieval-Augmented Generation (RAG) models for the automatic detection of signs of tuberculosis in chest X-ray images and the educational explanation of findings. The initial hypothesis was that it is possible to develop a functional prototype capable of recognizing pathological changes in X-ray images and generating informative, literature-grounded responses for users. Within this research, a CNN model for binary classification and YOLO models for the localization of pathological changes were developed and evaluated. The CNN model achieved an accuracy of 97% on the test set, representing a solid and measurable contribution. The YOLO models adequately demonstrated the concept of localization, with certain limitations related to dataset size and class imbalance. In addition to the visual module, a RAG prototype was implemented, utilizing a local medical document base to generate responses to user queries. The integration was implemented at the prototype level, without clinical validation. Based on the obtained results, the hypothesis was partially confirmed — to a significant extent for the CNN classification component within the test dataset used, while the YOLO and RAG components, due to dataset limitations and the absence of expert-verified reference answers, should be treated as proof-of-concept components. The thesis demonstrates that a modular combination of these technologies can serve as a useful foundation for the development of educational tools in the field of medical diagnostics.

On June 29, 2026, MSc candidate Anesa Abazović successfully defended her thesis entitled “Machine Learning and AI Model Development for Medical Applications” within the Artificial Intelligence Master’s programme at the University of Donja Gorica. Through its support for the programme, mentoring activities, and development of competencies in artificial intelligence and high-performance computing, NCC Montenegro contributes to preparing young researchers to apply advanced AI methods in medicine and other socially relevant domains. The thesis investigates the application of machine learning and deep learning to medical image analysis and clinical data classification, while also considering the technical, ethical, and practical challenges of integrating AI systems into healthcare.

ABSTRACT – This thesis explores the potential of machine learning (ML) and deep learning (DL) models in the detection of ovarian cancer and the prediction of pneumonia. In the first part, a YOLO model was used to identify tumor lesions in medical images, while in the second part, XGBoost, Random Forest, and neural network models were applied for the classification of clinical data. Model performance was evaluated using metrics such as precision, recall, accuracy, specificity, F1-score, ROC-AUC, MCC, mAP50, and mAP50-95. The experimental analysis demonstrated that AI models can achieve promising performance in both clinical scenarios, with certain limitations that require further validation. In addition to technical aspects, ethical considerations were also examined, including model interpretability, data privacy, and the integration of AI systems into healthcare information systems. It is concluded that AI can provide significant support to modern diagnostics, with the need for further improvements and clinical validation.