Liver Cirrhosis Stage Prediction Machine Learning Python Web App

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Liver cirrhosis is a widespread problem especially in North America due to high intake of alcohol. In this project, we will predict liver cirrhosis in a patient based on certain lifestyle and health conditions of a patient.

Cirrhosis is a late stage of scarring (fibrosis) of the liver caused by many forms of liver diseases and conditions, such as hepatitis and chronic alcoholism. The following data contains the information collected from the Mayo Clinic trial in primary biliary cirrhosis (PBC) of the liver conducted between 1974 and 1984. A description of the clinical background for the trial and the covariates recorded here is in Chapter 0, especially Section 0.2 of Fleming and Harrington, Counting Processes and Survival Analysis, Wiley, 1991. A more extended discussion can be found in Dickson, et al., Hepatology 10:1-7 (1989) and in Markus, et al., N Eng J of Med 320:1709-13 (1989).

A total of 424 PBC patients, referred to Mayo Clinic during that ten-year interval, met eligibility criteria for the randomized placebo-controlled trial of the drug D-penicillamine. The first 312 cases in the dataset participated in the randomized trial and contain largely complete data. The additional 112 cases did not participate in the clinical trial but consented to have basic measurements recorded and to be followed for survival. Six of those cases were lost to follow-up shortly after diagnosis, so the data here are on an additional 106 cases as well as the 312 randomized participants.

Dataset Description

The dataset for this competition (both train and test) was generated from a deep learning model trained on the Cirrhosis Patient Survival Prediction dataset. Feature distributions are close to, but not exactly the same, as the original. Feel free to use the original dataset as part of this competition, both to explore differences as well as to see whether incorporating the original in training improves model performance.


  • train.csv - the training dataset; Status is the categorical target; C (censored) indicates the patient was alive at N_DaysCL indicates the patient was alive at N_Days due to liver a transplant, and D indicates the patient was deceased at N_Days.
  • test.csv - the test dataset; your objective is to predict the probability of each of the three Status values, e.g., Status_CStatus_CLStatus_D.
  • sample_submission.csv - a sample submission file in the correct format

Running the web app


  • Install requirements
    pip install -r requirements.txt --user
  • Run flask web app


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