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cardiovascular-diseases

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Perform a survival analysis based on the time-to-event (death event) for the subjects. Compare machine learning models to assess the likelihood of a death by heart failure condition. This can be used to help hospitals in assessing the severity of patients with cardiovascular diseases and heart failure condition.

  • Updated Nov 4, 2023
  • Jupyter Notebook

This Project is based upon a CHDs (Cardiovascular Heart Diseases) research dataset which has over 3000 records and 16 attributes. Since, the target variable belongs to Categorical attribute, We built classification models for the future predictions of CHDs in patients considering the features.

  • Updated Oct 27, 2022
  • Jupyter Notebook

It is a Capstone project. A model has been created to predict for the heart diseases. It can be very useful for the health sector as cardiovascular diseases are rapidly increasing. The record contains patients' information. It includes over 4,000 records and 15 attributes.

  • Updated Feb 21, 2024
  • Jupyter Notebook

Supervised ML - Classification Using Python this project demonstrates the effectiveness of machine learning techniques in predicting cardiovascular risk using the Framingham Heart Study dataset. The developed machine learning model can be used by healthcare professionals to identify individuals at high risk of cardiovascular disease .

  • Updated Apr 5, 2024
  • Jupyter Notebook

CardioPulse ❤️🩺 is an Android app using machine learning to predict cardiovascular (heart) diseases. Created as a final year project by University of Sialkot students, it combines predictive modeling with Firebase cloud storage. The app offers personalized health assessments and intuitive tracking for early detection and better health outcomes.

  • Updated Sep 24, 2024
  • Java

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