<ul><li>The study explores the use of multiple data mining algorithms and deep learning for creating a prediction model for readmission rates in patients.</li><li>Various classifiers such as Logistic regression, Random forest, KNN, SVM, and Gaussian Naive Bayes are discussed for their application in prediction tasks.</li><li>Logistic regression is suitable for binary classification, Random forest combines predictions from multiple decision trees, KNN leverages nearest samples, SVM constructs separating hyperplanes, and Gaussian Naive Bayes assumes features follow a Gaussian distribution.</li><li>The study aims to evaluate the effectiveness of these algorithms in predicting patient readmission rates and determining the most appropriate approach for the dataset and research goals.</li></ul>

Overview of Machine Learning Classifiers Used in Readmission Prediction

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