**Personalized Medicine through Genomic Data Analysis**

Personalized Medicine through Genomic Data Analysis

Here's a Python snippet that employs machine learning to identify high-risk patients with cardiovascular disease based on genomic data:

from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import StandardScaler

# Train a random forest model on genomic data
rf_model = RandomForestClassifier(n_estimators=100)
rf_model.fit(X_train_scaled, y_train)

# Predict risk of cardiovascular disease for a new patient
patient_genomic_data = [[patient_data[0], patient_data[1], patient_data[2]]]
patient_risk = rf_model.predict(patient_genomic_data)

This code snippet uses a random forest model to classify patients with high-risk genomic profiles for cardiovascular disease. The StandardScaler is used to normalize the genomic data, ensuring that all features are on the same scale, which is essential for machine learning model training. The RandomForestClassifier is trained on a dataset containing genomic information and corresponding patient outcomes (e.g., cardiovascular disease status). Once trained, the model can be used to predict the risk of cardiovascular disease for a new patient based on their genomic profile.

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