Resources for 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.

R package for Kaplan-Meier Plot: Modified ggkm

Survival Analysis of Lung Cancer Patients

Kaplan-Meier-Estimator also known as the product limit estimator.

🍊 ➕ Survival Analysis add-on for Orange3 data mining suite.

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.

Survival Analysis

Survival Analysis of cancer patient data

survival curves in ggplot2

Survival analysis in R for Public Health (Imperial College London through Coursera)

Generating Kaplan Meier plots using gene expression data

Breast Cancer Survival Analysis using SAS on METABRIC dataset to identify key survival factors with Kaplan-Meier, Cox models, and gamma distributions

This project aims to analyze customer churn data to identify key drivers and suggest actionable strategies to improve retention.

HeartPredict is a Python library designed to analyze and predict heart failure outcomes using patient data.

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

Survival Analysis for Glioblastoma Multiforme

Graphical Presentation of Clinical Data in Oncology Studies Using R

Utilising a publicly-available and small dataset of ~5K patients from Kaggle, to practice health data analysis.