Survival Prediction Analysis Using Scikit-Learn, Scikit-Survival, and Lifelines by Olivier Grisel

Predictive survival analysis is an important area in machine learning, especially in healthcare and medical research. It involves predicting the time until a specific event of interest occurs to an individual, such as the time until a patient develops a disease, or the time until a component of a machine fails. Olivier Grisel is a well-known researcher and developer in the field of machine learning, and he has contributed to the development of scikit-learn, scikit-survival, and lifelines, which are Python libraries that are commonly used for predictive survival analysis.

In this tutorial, we will go through the process of performing predictive survival analysis using these three libraries, focusing on how to preprocess the data, train a model, and evaluate its performance using relevant metrics. By the end of this tutorial, you should have a good understanding of how to use these libraries to perform predictive survival analysis.

1. Installing the necessary libraries
   To get started, you need to install the following libraries:
   • scikit-learn
   • scikit-survival
   • lifelines

You can install these libraries using pip by running the following command:

pip install scikit-learn scikit-survival lifelines

1. Loading and preprocessing the data
   For this tutorial, we will use the Breast Cancer dataset from the scikit-survival library. This dataset contains features related to breast cancer patients, as well as the time until their death or last follow-up. To load the dataset, run the following code:

from sksurv.datasets import load_breast_cancer

data_x, data_y = load_breast_cancer()

Next, we need to preprocess the data by splitting it into training and testing sets, and by encoding categorical variables. Here is an example code snippet that performs these preprocessing steps:

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sksurv.preprocessing import OneHotEncoder

X_train, X_test, y_train, y_test = train_test_split(data_x, data_y, test_size=0.2, random_state=42)

# Encode categorical variables
categorical_columns = data_x.select_dtypes(include='object').columns
encoder = OneHotEncoder()
encoder.fit(X_train[categorical_columns])
X_train_encoded = encoder.transform(X_train)
X_test_encoded = encoder.transform(X_test)

# Standardize numerical columns
numerical_columns = data_x.select_dtypes(include=['int64', 'float64']).columns
scaler = StandardScaler()
scaler.fit(X_train[numerical_columns])
X_train_scaled = scaler.transform(X_train_encoded)
X_test_scaled = scaler.transform(X_test_encoded)

1. Training a survival model
   We will now train a survival model using the scikit-survival library. In this tutorial, we will use the Cox proportional hazards model, which is a popular model for survival analysis. Here is an example code snippet that trains a Cox model on the preprocessed data:

from sksurv.linear_model import CoxPHSurvivalAnalysis

estimator = CoxPHSurvivalAnalysis()
estimator.fit(X_train_scaled, y_train)

1. Evaluating the model
   Once the model is trained, we need to evaluate its performance using relevant metrics such as the concordance index (C-index) and the Brier score. Here is an example code snippet that calculates these metrics on the test set:

from sksurv.metrics import concordance_index_censored, brier_score

prediction = estimator.predict(X_test_scaled)
c_index = concordance_index_censored(y_test['event'], y_test['time'], prediction)
brier_score = brier_score(y_test['event'], y_test['time'], prediction)

print("Concordance Index:", c_index[0])
print("Brier Score:", brier_score[0])

1. Visualizing the results
   Finally, we can visualize the survival probabilities predicted by the model using the lifelines library. Here is an example code snippet that generates a Kaplan-Meier plot of the predicted survival probabilities:

from lifelines import KaplanMeierFitter

kmf = KaplanMeierFitter()
kmf.fit(prediction['time'], event_observed=prediction['event'])
kmf.plot()

And that’s it! You have now learned how to perform predictive survival analysis using scikit-learn, scikit-survival, and lifelines. You can further explore these libraries and experiment with different models, hyperparameters, and evaluation metrics to improve the performance of your survival analysis models.
I hope you found this tutorial helpful. Thank you for reading!