Classification of stroke patients using data mining with adaboost, decision tree and random forest models

A stroke is a fatal disease that usually occurs to the people over age of 65. The treatment progress medical field growing rapidly, especially with technological advance, emergence various record data sets can be used in records identify trends these using mining. purpose this study was propose model classify survivors mining, by utilizing from kaggle sharing dataset. models proposed were AdaBoost, Decision Tree and Random Forest, evaluation results Confusion Matrix ROC Analysis. obtained decision tree able provide best accuracy compared other models, which 0.953 for Number Folds 5 10. From study, good classification sufferers.