Real-time Fall Detection System using Arduino, MPU6050 sensor, and TinyML for wearable safety applications.
AI healthcare platform for symptom analysis, disease prediction, risk assessment, and patient health management, with ML-powered predictions delivered through a FastAPI backend and React frontend. Expanding toward personalized recommendations, healthcare reporting, and analytics.
Machine Learning Based Diabetes Risk Prediction System using Python, Flask and Random Forest. Includes data preprocessing, feature scaling and an interactive web interface for diabetes risk assessment.
Healthcare data analysis using Python, PySpark, and Spark SQL.
An AI-powered system that predicts cancer type from a patient’s somatic mutation profile using gene signatures, SHAP-based feature importance, and tumor mutation burden (TMB). It provides interpretable results with confidence scores, top predictions, and explanations, all through an interactive Streamlit web interface.
Plataforma de Hospitalización Domiciliaria y Gestión Clínica en .NET 8. Implementa Arquitectura N-Capas, EF Core con SQL Server 2022 en Docker, Razor Pages, Patrón Repositorio Genérico y suite de pruebas xUnit.
Comparative analysis of 5 ML models (LightGBM, XGBoost, Random Forest, SVM, Logistic Regression) across 3 chronic disease datasets with SHAP explainability. Research paper included.
SHWASA
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