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healthcare-analytics

Here are 16 public repositories matching this topic...

CHB-MIT-data-preprocessing-and-prediction

This project focuses on data preprocessing and epilepsy seizure prediction using the CHB-MIT EEG dataset. It includes steps like data cleansing, feature extraction, and handling imbalanced datasets, aimed at improving the accuracy of seizure prediction.

  • Updated Nov 22, 2023
  • Python

Heart disease is still a major worldwide health concern since it is one of the leading causes of mortality and morbidity in India. Early and precise diagnosis of heart disease can save lives and reduce medical costs. Conventional diagnostic methods, however, are often expensive and need specific equipment and expertise.

  • Updated Jun 15, 2024
  • Jupyter Notebook

Hospital database system built with Oracle APEX and SQL, featuring an interactive dashboard for real-time insights into patient distribution and doctor availability. Designed to optimize resource management and support hospital administration in data-driven decision-making.

  • Updated Oct 11, 2024
  • PLSQL

This repository contain projects completed during my graduate study in Data Science & Analytics at the J. Mack Robinson College of Business, Georgia State University. I worked as part of a team of 4 or 6 members and we equally contributed in completing tasks and preparing final documentations (code file, report & PowerPoint presentation).

  • Updated Nov 24, 2021
  • Jupyter Notebook

Análisis predictivo de trasplante de médula ósea en pacientes pediátricos utilizando modelos de regresión logística y random forest. Incluye análisis y visualizaciones de datos clínicos para predecir la recaída post-trasplante.

  • Updated Sep 2, 2024

A comprehensive project showcasing the development of a Hospital Data Warehouse for Services Hospital Lahore, featuring ETL workflows, data mining with KNIME, and interactive Power BI visualizations to improve operational efficiency and patient care.

  • Updated Dec 18, 2024

This project aims to predict the likelihood of a heart attack based on various health indicators using machine learning techniques. The dataset used contains patient data with features such as age, cholesterol levels, blood pressure, and more.

  • Updated Sep 20, 2024
  • Jupyter Notebook

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