A systems-level analysis engine that models sleep as a recovery debt process rather than a nightly outcome. Using physiological traits and ecological pressure signals, it estimates predicted sleep need, quantifies sleep debt, and visualizes how stress accumulates silently before visible fatigue or failure occurs.

Privacy-first personal health journal with experimental AI features. Track medications, journal symptoms, and explore on-device ML (for educational purposes)

The swiss army knife for discharge abstract database (DAD).

Patient Reported Information Multidimensional Exploration (PRIME) is an automated platform to investigate Online Support Group (a.k.a. health forums, online health groups) discussions for investigation of individualised patient behaviours and patient information, over time.

MortalityMinder: an R-based, multi-view interactive presentation examining county-level factors associated with midlife mortality trends.

The W4H Integrated Toolkit Repository provides a unified platform for managing, analyzing, and visualizing wearable health data using a suite of open-source tools and frameworks.

Milestone 2 project - Electronic Health Record Analysis and ICD Code Prediction

ClinicCare é um sistema de gestão para clínicas e consultórios, com agendamento, prontuários eletrônicos, controle financeiro e relatórios interativos. Desenvolvido com Dash e Plotly, possui interface responsiva, validações avançadas e foco em eficiência e segurança.

Data analysis focusing on health problems

Self-hosted fitness tracker with AI workout generation, medication tracking, gamification, and privacy-first design. Track workouts, medications, correlations, and compete on leaderboards—all on your own infrastructure.

Comprehensive collection of 8 clinical data science and health analytics projects focusing on disease prediction, risk stratification, and treatment pattern analysis using advanced machine learning algorithms and statistical modeling. Portfolio: https://nana-safo-duker.github.io/

An interactive web dashboard that analyzes Fitbit wearable data to provide personalized health insights, including activity trends, sleep correlations, and AI-powered calorie burn predictions.

This project analyses the 2015 BRFSS health survey to identify key predictors of diabetes risk using data cleaning, statistical analysis, and machine‑learning models. BRFSS stands for Behavioral Risk Factor Surveillance System — a large, annual telephone‑based health survey run by the U.S. Centers for Disease Control and Prevention (CDC).

Machine learning project to predict obesity risk levels based on lifestyle and demographic data. This project utilizes advanced algorithms like CatBoost, LightGBM, and more to classify individuals into different obesity categories

Developed a machine learning model for diabetes prediction, using patient data to provide early risk assessments and improve healthcare outcomes.

The app helps to analyze correlation between various weather parameters and symmetry of electrocardiographic T-wave

Ultra-Processed Foods Analysis using Machine Learning to uncover nutritional patterns and hidden associations for health insights.

Interactive epidemiological dashboard visualizing disease trends across Kenya. Built with PHP, Chart.js, and Leaflet for the CEMA Software Engineering Internship.

Visualized 2015 U.S. health survey data to uncover risk factors and patterns associated with diabetes.

SQL database and analysis of CDC BRFSS Alzheimer’s & Healthy Aging data examining demographic and lifestyle factors associated with cognitive decline using SQL views, procedures, and functions.