Stroke Risk Prediction Machine Learning Techniques
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Questions and Answers

What is highlighted as a key challenge when predicting stroke using machine learning techniques?

  • Collecting large amounts of data
  • Dealing with data imbalance and outliers (correct)
  • Selecting the best algorithm
  • Ensuring interpretability of the model

    • Which algorithms were compared in the study for predicting stroke?

  • Logistic Regression, Decision Trees, and Naive Bayes
  • Linear Regression, Clustering, and PCA
  • Neural Networks, Gradient Boosting, and Anomaly Detection
  • SVM, Random Forest, and KNN (correct)

    • What aspect is identified as a gap in the current research on machine learning for stroke prediction?

  • Inability to predict stroke accurately
  • Lack of constructing a full recommendation system (correct)
  • Not using machine learning algorithms
  • Failure to address data preprocessing steps

    • How can a recommendation system based on machine learning models change stroke management?

    <p>By offering individualized treatment alternatives</p> Signup and view all the answers

    Which technique has the potential to not only forecast the possibility of a stroke but also provide targeted preventive and recovery strategies?

    <p>A recommendation system combining predictive analytics</p> Signup and view all the answers

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