sauravmishra1710 / Heart-Failure-Condition-And-Survival-Analysis

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.

Date Created 2020-10-10 (3 years ago)

Commits 239 (last one 2 years ago)

Stargazers 40 (0 this week)

Watchers 1 (0 this week)

Forks 15

License unknown

Ranking

RepositoryStats indexes 565,600 repositories, of these sauravmishra1710/Heart-Failure-Condition-And-Survival-Analysis is ranked #499,017 (12th percentile) for total stargazers, and #521,698 for total watchers. Github reports the primary language for this repository as Jupyter Notebook, for repositories using this language it is ranked #13,477/16,305.

sauravmishra1710/Heart-Failure-Condition-And-Survival-Analysis is also tagged with popular topics, for these it's ranked: machine-learning (#7,060/7699), data-visualization (#819/882), machine-learning-algorithms (#250/263)

All Topics

lgbm heartfailure time-to-event cox-regression machine-learning kaplan-meier-plot kaplanmeierfitter survival-analysis xgboost-algorithm data-visualization healthcare-datasets healthcare-application cardiovascular-diseases exploratory-data-analysis machine-learning-algorithms

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231 commits on the default branch (master) since jan '22

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The primary language is Jupyter Notebook but there's also others...

updated: 2024-09-21 @ 01:34pm, id: 302879721 / R_kgDOEg2T6Q