AI in Healthcare

What is the main purpose of text preprocessing in the disease prediction model?

• To increase the size of the dataset.

• To convert raw text into a format suitable for machine learning models.

• To identify diseases directly from symptoms.

• To predict the accuracy of the model.

Which of the following steps is NOT part of the text preprocessing pipeline?

• Tokenization

• Vectorization

• Model Training

• Stemming

What is the role of CountVectorizer in the model

• To preprocess text by stemming and lemmatization.

• To convert text data into numerical feature vectors.

• To predict diseases based on symptoms.

• To split the dataset into training and testing sets.

Why do we use the LogisticRegression model in the project?

• It is designed for text preprocessing tasks.

• It performs well for multiclass classification problems.

• It reduces the number of tokens in the dataset.

• It is faster than all other machine learning models.

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