Robust classification model for identifying stroke patients utilising a machine learning-based ensemble stacking method

Abstract Purpose of the study : Cerebrovascular accident (CVA) occurs quickly and disrupts brain blood flow, causing neurological problems. Risk factors include smoking, diabetes, hyperlipidemia, hypertension, and atrial fibrillation. Many brain stroke prediction systems are computationally demanding, sluggish, and unreliable. This study uses interpolation spline curves that impute data and Principal Component Analysis to identify the most important factors for stroke prediction. Using stack ensemble machine learning methods, the goal is to improve the precision of stroke disease identification. Used method: Using interpolation spline curve data imputation and Principal Component Analysis to preprocess and extract EHR data is unique. In addition, this article also proposes a novel stacking ensemble technique using bagging and boosting as foundation learners. Bagging and boosting enhance prediction by reducing variation and bias. Stack-based ensemble learning classifies brain strokes. Build base and meta-learners with Random Forest, XGBoost, Decision tree classifier, KNN, and Logistic Regression. The evaluation of the efficiency of the machine learning model determines the optimal stacking ensemble meta-learner model. Brief Description of Results: The results emphasize the best meta-learner model of the stacking ensemble and the impact of the imputation technique on the representation of the electronic health record. Using PCA, the most important factors have been identified for detecting stroke. The results of the analysis showed that the logistic regression as a meta-educator had the best predictive performance, achieving 0.96 precision and 0.98 recall value, respectively, with 97% accuracy and AUC curve value of 0.99. Findings: The Decision Tree Classifier and K-Nearest Neighbor as a metalearner got 95%, 96% accuracy and both 0.95 AOC curve values in this data set, showing that the method works. Precision values of 0.93, and 0.95 for the Decision Tree Classifier and KNN Classifiers as meta-learner confirm their robustness. These findings suggest that interpolation spline curve and stacking-based ensemble machine learning can improve the identification of brain stroke from incomplete electronic health records.