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)

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.

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

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

Data analysis focusing on health problems

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.

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.

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

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

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

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

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

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.

Mini Case Study is from Danny Ma's 8 Week SQL Challenge

Multi-model health analysis using a 10k global dataset. Features both Classification (Random Forest, SVC, MLP) and Regression (Linear, SVR, Decision Tree, Kernel Ridge, Neural Networks) to predict health risks and BMI trends based on caffeine and lifestyle habits.

An end-to-end machine learning project to predict Autism Spectrum Disorder (ASD) risk in adults. Features a full ETL pipeline, comparative analysis of unsupervised & supervised models, and a final multiclass predictor.