This paper presents the development and implementation of an intelligent system for predicting the risk of diabetes spread using machine learning techniques. The core of the system relies on the analysis of the Pima Indians Diabetes dataset through k-nearest neighbours (k-NN), Random Forest, Logistic Regression, Decision Trees and XGBoost algorithms. After pre-processing the data, including normalization and handling missing values, the k-NN model achieved an accuracy of 77.2%, precision of 80.0%, recall of 85.0%, F1-score of 83.0% and ROC of 81.9%. The Random Forest model achieved an accuracy of 81.0%, precision of 87.0%, recall of 91.0%, F1-score of 89.0% and ROC of 90.0%. The Logistic Regression model achieved an accuracy of 60.0%, precision of 93.0%, recall of 61.0%, F1-score of 74.0% and ROC of 69.0%. The Decision Trees model achieved an accuracy of 79.0%, precision of 87.0%, recall of 89.0%, F1-score of 88.0% and ROC of 83.0%. In comparison, the XGBoost model outperformed with an accuracy of 83.0%, precision of 85.0%, recall of 96.0%, F1-score of 90.0% and ROC of 91.0%, indicating strong prediction capabilities. The proposed system integrates both hardware (continuous glucose monitors) and software (AI-based classifiers) components, ensuring real-time blood glucose level tracking and early-stage diabetes risk prediction. The novelty lies in the proposed architecture of a distributed intelligent monitoring system and the use of ensemble learning for risk assessment. The results demonstrate the system's potential for proactive healthcare delivery and patient-centred diabetes management.

This paper presents the development of an intelligent information system for analysing the happiness index and life satisfaction based on sociological survey data from various countries. The research addresses the need to improve the accuracy and efficiency of social research by integrating data mining and machine learning methods – specifically K-means clustering and multiple regression analysis – into the system design. The proposed module enables automated classification of countries and cities by life satisfaction levels, allowing stakeholders to make informed decisions on urban planning and social policy. The system also facilitates the identification of favourable living environments, providing valuable insights into the social, economic, and environmental factors affecting well-being. The experimental results on real-world datasets confirm the module’s effectiveness and predictive capabilities.