Project using machine learning to predict depression using health care data from the CDC NHANES website. A companion dashboard for users to explore the data in this project was created using Streamlit. Written with python using jupyter notebook for the main project flow/analysis and visual studio code for writing custom functions and creating th…

Hackathon notes

jupyter-eds-notebooks provides Docker images with preconfigured Jupyter environments for clinical and health data analysis, tailored for AP‑HP Datalabs and the HELIX platform.

This repository features cutting-edge machine learning applications in healthcare, addressing diverse challenges such as dermatological lesion detection, ECG signal categorization, gland segmentation in colorectal cancer, pathological myopia prediction, and pneumothorax identification.

Notebooks supporting investigations in U.S. healthcare data.

Jupyter notebook to analyize, transform, and predict intracranial hemorrhaging from CT scan images using a neural network.

A self-care journal template for digital notebooks

A very interesting repo towards Alzheimer disease (Healthcare) contains 2 important Notebooks one with handling the imbalance data and other without significantly handling the imbalance.

End‑to‑end breast cancer prediction workflow with reproducible notebooks and Streamlit dashboard.

This notebook is designed to analyze and visualize the comorbidity indices of patients over time.

BMI stands as Body Mass Index Calculator. I have created this using python and jupyter notebook as the workspace.

A notebook exploring a MLP Neural Network for predicting the presence of heart disease based on various health metrics

Dr. KYMN (Kentucky Medicaid Notebook) is a living resource built to help Kentuckians understand their Medicaid options in plain language.

A reproducible notebook that explores heart-failure clinical data, performs feature engineering, and builds an XGBoost model to estimate patient risk.

❤️ Predict heart disease risk using classic machine learning techniques with this Jupyter notebook project, featuring data exploration and model building.

Practice notebook on heart-disease risk with a small/noisy dataset: EDA → preprocessing → classic ML baselines (scikit-learn). Not for clinical use

A comprehensive health data analysis project using PySpark and TensorFlow for medical diagnosis and outcome prediction. Features large-scale data processing and interactive notebooks.

🐙 Lung cancer prediction with logistic regression using clinical features and feature selection, with robust evaluation metrics. Code and results live in the analysis notebook.

🫀 Production-ready ML system for heart disease risk assessment with 88.5% accuracy. Features FastAPI REST API, Streamlit dashboard, SHAP explainability, MLflow tracking, and Docker deployment. Demonstrates end-to-end ML engineering from notebook to production-grade application.

Interpretable ML pipeline (LogReg, RF, XGBoost) to identify Alzheimer’s risk factors using SHAP, LIME, and fairness analysis across demographics. Includes preprocessing, EDA, model training, and explainability notebooks.