Artificial Intelligence (AI) methodologies have profoundly influenced healthcare research, particularly in chronic disease management and public health. This paper provides a comprehensive state-of-the-art review of AI’s applications across diabetes, cancer, epidemiology, and mortality prediction. The analysis highlights advancements in machine learning (ML), deep learning (DL), and natural language processing (NLP) that enable robust predictive models and decision support systems, leading to significant clinical and public health outcomes. The study examines predictive modeling, pattern recognition, and decision support applications, addressing their respective challenges and potential in real-world healthcare settings. Emphasis is placed on the emerging role of explainable AI (XAI), multimodal data fusion, and privacy-preserving techniques such as federated learning, which aim to enhance interpretability, robustness, and ethical compliance. This paper underscores the vital role of interdisciplinary collaboration and adaptive AI systems in creating resilient, scalable, and patient-centric healthcare solutions.

Leveraging Machine Learning to Unveil the Critical Role of Geographic Factors in COVID-19 Mortality in Mexico

This paper presents a web-based intelligent analytics system that integrates automated data ingestion, preprocessing, distributional analysis, algorithm recommendation, and model execution within a user-friendly interface. The platform supports common data formats and guides users through statistical checks such as normality tests, ANOVA, and correlation diagnostics, ensuring analytical rigor. It incorporates machine learning techniques including KMeans, SVM, and regression models, automatically recommending appropriate methods based on dataset characteristics. A key innovation is the seamless integration of statistical assumption validation with ML workflows, enabling users without technical expertise to perform reproducible analyses. We demonstrate applicability on COVID-19 datasets and financial time series, highlighting its utility in real-world data science scenarios.