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