Resources for Survival Analysis
survival-analysis cox-regression competing-risks kaplan-meier-plot proportional-hazards-models time-dependent-covariates time-to-event-prediction
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kaplan-meier-plot
Here are 22 public repositories matching this topic...
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.
machine-learning exploratory-data-analysis machine-learning-algorithms data-visualization survival-analysis healthcare-datasets cox-regression healthcare-application time-to-event xgboost-algorithm lgbm kaplan-meier-plot heartfailure cardiovascular-diseases kaplanmeierfitter
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- Jupyter Notebook
R package for Kaplan-Meier Plot: Modified ggkm
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- R
Survival Analysis of Lung Cancer Patients
python data-science survival-analysis survival kaplan-meier cox-model cancer-patients survival-models log-rank-test survival-prediction kaplan-meier-plot cox-proportional-hazard cox-proportional-hazards coxph-model kaplanmeierfitter nelsonaalenfitter
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- Jupyter Notebook
🍊 ➕ Survival Analysis add-on for Orange3 data mining suite.
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- Python
Kaplan-Meier-Estimator also known as the product limit estimator.
statistics genomics data-visualization medical stats survival kaplan-meier genomics-visualization kaplan-meier-plot
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- TypeScript
survival curves in ggplot2
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- R
Graphical Presentation of Clinical Data in Oncology Studies Using R
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- R
Breast Cancer Survival Analysis using SAS on METABRIC dataset to identify key survival factors with Kaplan-Meier, Cox models, and gamma distributions
sas breast-cancer clinical-data gamma-distribution kaplan-meier-plot cox-proportional-hazard hazard-plots
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- SAS
Use of clinical EHR data and codelists to investigate all-cause mortality between proton pump inhibitor (PPI) and histamine H2-receptor antagonist (H2RA) exposure groups.
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Survival analysis techniques applied to the GBSG2 breast cancer dataset. It covers Kaplan-Meier estimators, parametric models like Weibull and Log-Normal, and the Cox Proportional Hazards model. Key insights include the impact of tumor size and hormonal therapy on survival, supported by detailed visualizations
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- Jupyter Notebook
Generating Kaplan Meier plots using gene expression data
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- R
HeartPredict is a Python library designed to analyze and predict heart failure outcomes using patient data.
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- Jupyter Notebook
📊 Analyze cardiac disease data to find patterns and risk factors, ensuring clean data and clear insights for better health outcomes.
javascript programming-language machine-learning django analysis random-forest exploratory-data-analysis data-visualization survival-analysis healthcare-datasets cox-regression time-to-event xgboost-algorithm kaplan-meier-plot heart-disease-predictor heart-health-prediction heartfailure cardiovascular-diseases
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- Python
Survival Analysis for Glioblastoma Multiforme
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- Python
Utilising a publicly-available and small dataset of ~5K patients from Kaggle, to practice health data analysis.
biostatistics survival-analysis kaplan-meier stroke medical-informatics kaplan-meier-plot q-q-plot stroke-prediction
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- HTML
This project aims to analyze customer churn data to identify key drivers and suggest actionable strategies to improve retention.
visualization statistical-models customer-segmentation churn-analysis seaborn-plots kaplan-meier-plot
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- Jupyter Notebook
Survival Analysis of cancer patient data
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- Jupyter Notebook
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