Framework to build, evaluate, select, and compare ML survival analysis models using high-dimensional biological data and other covariates

This is my attempt on the Titanic Machine Learning challenge in Kaggle and Breast Cancer Prediction Challenge in Topcoder.

Survival probability of burn injury patients based on age, sex, race, hospital facility, and other significant facility nearing respondents.

Repository containing reinforcement learning experiments for SMART-ACT project using the QuBBD data

In this work I tested if there was an improvement in performance in the use of different survival models.

Project scope: Prediction if the bank's client will or will not leave the bank.

Machine learning project to predict survival outcomes for cirrhosis patients using K-Nearest Neigbors (KNN), Decision Tree, and Support Vector Machine (SVM) models, based on UCI's clinical dataset.

Example code supporting immunology research

Automating the prognosis of cancer in new patients and also survival prediction of existing cancer patients to see whether they fall into relapse or non-relapse and provide appropriate treatment. We have introduced a new idea , where an e-commerce application using micro services approach has been developed to track the purchases of the users an…

A course project for estimating the parameters of a Weibull Distribution which models the survival rates of cancer using Maximum Likelihood Estimation

Investigation of inflation of Integrated Brier Score for validation of survival models

Survival Prediction on the Titanic Dataset

This repository includes the different files used for the master thesis "Dynamic updating of survival prediction models in a pademic setting" by Claudine Stark (performed at LUMC) as part of the Master Statistics and Data Science at Leiden University.

Offical repo of the paper "A novel methodological framework for the analysis of health trajectories and survival outcomes in heart failure patients" (ICLR 2024)

A Flask web app that provides time-of-sale estimates for home listings in the Calgary market. The model used by Sale A-When is the result of a survival analysis carried out on a large sales data set.

this repository hold the supporting code for the blog post

AI pitch for survival ML (PANCAIM)

Time-related survival prediction in molecular subtypes of breast cancer using time-to-event deep-learning-based models.

This project develops predictive maintenance models for industrial robots in nuclear fuel replacement, leveraging data analytics, machine learning, and decision-making frameworks to optimize robot fleet management and extend operational uptime. Key phases include data exploration, feature engineering, RUL prediction, and maintenance decision-making