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.
Real-time Fall Detection System using Arduino, MPU6050 sensor, and TinyML for wearable safety applications.
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.
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
55 Claude skills for specialized roles. Legal, healthcare, HR, operations, real estate, government consulting, and cross-industry workflows
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.
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