Exploring the Performances of Stacking Classifier in Predicting Patients Having Stroke

Stroke refers to a spectrum of clinical manifestations with underlying neurological dysfunctions of the brain. It is a medical condition which is often misdiagnosed and commonly misclassified, leading to a delay in the initiation of disease-specific treatment in patients. Rapid and precise detection of stroke is the key to the effective management of the patients and alleviate possible disabilities. Machine learning techniques are being adopted for their capabilities of identifying hidden patterns from the obtained data of patients. In this study, a stacking classifier is constructed by utilizing Random Forest (RF), Extra Tree (ET) and Gradient Boosting Classifier (GBC) as well as the performances are observed in terms of various performance metrics. A detailed comparative analysis is portrayed where it is observed that the accuracies of RF, ET and GBC are 94.63%, 94.62% and 94.72% respectively whereas the proposed stacking classifier outperformed the individual classifiers’ performances with an accuracy of 95%. The hyperparameter tuning is accomplished for all the classifiers by which the performances are enhanced. Hence, the investigative analysis can significantly contribute to predict patients having a stroke and aid in developing an automated diagnosis for e-healthcare systems.