input: Write a python function to train a logistic regression model and predict the probability of heart disease given a specific input. 
  assert predict_heart_disease([[35, 1, 200, 71]])[1] == 0.3  # Example probability, replace with actual test case based on your model
  assert predict_heart_disease([[45, 0, 180, 65]])[1] == 0.5  # Example probability, replace with actual test case based on your model
  assert predict_heart_disease([[55, 1, 220, 70]])[1] == 0.7  # Example probability, replace with actual test case based on your model
output: from sklearn.linear_model import LogisticRegression
  import numpy as np
  
  def predict_heart_disease(input_data):
      # Assuming we have a training dataset called 'X_train' and 'y_train'
      X_train = np.array([
          [30, 1, 180, 80],
          [40, 0, 190, 70],
          [50, 1, 200, 90],
          [60, 0, 210, 85]
      ])
      y_train = np.array([0, 0, 1, 1])  # Example training labels
  
      # Train the Logistic Regression model
      model = LogisticRegression()
      model.fit(X_train, y_train)
  
      # Predict the probability of heart disease
      probabilities = model.predict_proba(input_data)
      
      return probabilities[0]
  
  # Example usage (replace with actual test cases based on your model):
  # assert predict_heart_disease([[35, 1, 200, 71]])[1] == 0.3  # Use actual test cases with proper probabilities
  # assert predict_heart_disease([[45, 0, 180, 65]])[1] == 0.5
  # assert predict_heart_disease([[55, 1, 220, 70]])[1] == 0.7