Healthcare Data Management - Predictive Health Risk Model

 Notes:

• What problem does it solve?: It uses patient data to predict health risks, allowing healthcare providers to take proactive steps.

• How can businesses or users benefit from customizing the code?: Healthcare providers can customize the model for different diseases or conditions by training it on more specific datasets.

• How can businesses or users adopt the solution further, if needed?: The solution can be further expanded to integrate with electronic health records (EHR) for real-time predictions.

Python Code:

import pandas as pd

from sklearn.model_selection import train_test_split

from sklearn.ensemble import RandomForestClassifier

# Example dataset

data = pd.DataFrame({

    'age': [25, 45, 65, 35, 55],

    'blood_pressure': [120, 140, 160, 130, 150],

    'cholesterol': [200, 250, 280, 220, 260],

    'risk': [0, 1, 1, 0, 1]  # 0: low, 1: high

})

X = data[['age', 'blood_pressure', 'cholesterol']]

y = data['risk']

# Train a predictive model

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

model = RandomForestClassifier()

model.fit(X_train, y_train)

# Predict health risk for a new patient

new_patient_data = pd.DataFrame({'age': [60], 'blood_pressure': [155], 'cholesterol': [270]})

prediction = model.predict(new_patient_data)

print("Predicted Health Risk:", "High Risk" if prediction[0] == 1 else "Low Risk")