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Questions and Answers
What is highlighted as a key challenge when predicting stroke using machine learning techniques?
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?
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?
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?
How can a recommendation system based on machine learning models change stroke management?
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Which technique has the potential to not only forecast the possibility of a stroke but also provide targeted preventive and recovery strategies?
Which technique has the potential to not only forecast the possibility of a stroke but also provide targeted preventive and recovery strategies?
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