Predicting Patient Readmission with Machine Learning – Healthcare Tutorial

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Predicting Patient Readmission with Machine Learning – Healthcare Tutorial

Predicting Patient Readmission with Machine Learning

Healthcare ML Infographic
Dataset: UCI Diabetes Readmission Dataset

1. Why Predict Patient Readmission?

Patient readmission refers to when a patient who has been discharged from a hospital is admitted again within a short period—typically 30 days. Frequent readmissions can indicate poor care quality, ineffective discharge planning, or underlying chronic conditions. Using ML to predict readmissions can save hospitals costs and improve patient outcomes.

2. Importing Essential Libraries

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

import seaborn as sns

from sklearn.model_selection import train_test_split

from sklearn.ensemble import RandomForestClassifier

from sklearn.metrics import classification_report, confusion_matrix

3. Loading and Understanding the Data

df = pd.read_csv('diabetic_data.csv')

print(df.shape)

print(df.columns)

The dataset contains over 100,000 hospital encounters with more than 50 features related to diagnosis, procedures, medications, etc.

4. Data Cleaning

Remove irrelevant or high-missing columns like 'weight', 'payer_code', 'medical_specialty'.

df = df.drop(['weight', 'payer_code', 'medical_specialty'], axis=1)

df.replace('?', np.nan, inplace=True)

df = df.dropna()

5. Target Variable

We will transform the 'readmitted' column to a binary label.

df['readmitted'] = df['readmitted'].apply(lambda x: 1 if x == '<30' else 0)

6. Feature Encoding

Convert categorical variables into numerical format.

df = pd.get_dummies(df, drop_first=True)

7. Train-Test Split

X = df.drop('readmitted', axis=1)

y = df['readmitted']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

8. Model Building

model = RandomForestClassifier(n_estimators=100, random_state=42)

model.fit(X_train, y_train)

y_pred = model.predict(X_test)

9. Evaluation

print(confusion_matrix(y_test, y_pred))

print(classification_report(y_test, y_pred))

10. ROC & AUC

from sklearn.metrics import roc_auc_score, roc_curve

y_prob = model.predict_proba(X_test)[:,1]

roc_auc = roc_auc_score(y_test, y_prob)

print("AUC: ", roc_auc)

11. Model Explainability with SHAP

import shap

explainer = shap.TreeExplainer(model)

shap_values = explainer.shap_values(X_test)

shap.summary_plot(shap_values[1], X_test)

12. Deployment (Optional)

You can deploy this model using Flask or FastAPI on a cloud service like Heroku or Render.

13. Conclusion

This project demonstrated how to build an end-to-end ML pipeline to predict hospital readmissions. It covered data preprocessing, model training, evaluation, and interpretation. Accurate predictions can assist healthcare providers in planning better post-discharge strategies and reducing costs.

14. Further Reading

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