Stroke Patients Classification Using Resampling Techniques and Decision Tree Learning

Stroke Patients Classification Using Resampling Techniques and Decision Tree Learning

© 2021 by IJETT Journal
Volume-69 Issue-6
Year of Publication : 2021
Authors : Sumitra Nuanmeesri, Wongkot Sriurai, Nattanon Lamsamut
DOI :  10.14445/22315381/IJETT-V69I6P217

How to Cite?

Sumitra Nuanmeesri, Wongkot Sriurai, Nattanon Lamsamut, "Stroke Patients Classification Using Resampling Techniques and Decision Tree Learning," International Journal of Engineering Trends and Technology, vol. 69, no. 6, pp. 115-120, 2021. Crossref,

Stroke is a major global and worldwide public health problem. It is a major cause of mortality, morbidity, and disability in developed and increasingly in less developed countries. The goal of this study is to develop a classification model for stroke patients towards the application of resampling techniques together with the decision tree learning methods. Since the size of the collected dataset to construct the model was small, the research team applied the resampling techniques to solve the problem. When the datasets of predicted outputs were imbalanced, the data size needs balance adjustment between 100%-300%. Afterward, decision tree learning was applied to the construction of the classification model for stroke patients by which the results from three decision tree learning methods, including ID3, C4.5, and Random Forest, were compared. The model’s effectiveness was evaluated by 10-fold cross-validation. The evaluation results showed that the model tested with 10-fold cross-validation and adjusted by resampling to 200% with the Random Forest technique provided the highest level of effectiveness, with the classification accuracy of 96.40%, precision of 96.45%, and recall of 96.60%. This model gave higher efficiency than the results gained from both ID3 and C4.5 techniques.

Cerebrovascular Disease, Decision Tree, Patient Classification, Resampling, Stoke

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