Optistroke: Harnessing Bat Algorithm-Driven Stacked Ensembles for Enhanced Stroke Prediction Using Machine Learning
Keywords:
Stacked Ensemble, Gradient Descent, Prediction, Optimization, Machine Learning, Healthcare, StrokeAbstract
Strokes are considered to be one of the most serious medical conditions in the world and must be diagnosed at an early stage so that the consequences for the patients can be minimized. The proposed BatOptiStroke is a novel technique that increases stroke prediction accuracy by using a stacked ensemble model powered by the Bat Algorithm (BA). To capture a wide range of prediction skills, BatOptiStroke combines a diversified selection of base models, such as Extreme Gradient Boosting (XGBoost), K Nearest Neighbors (KNN), and Support Vector Machines (SVM). By modifying the placements and velocities of the bats that reflect the fundamental models, the BA continuously optimizes the group's efficiency. This results in increased stroke prediction accuracy. On a sizable dataset of stroke patients, the BatOptiStroke framework's efficiency is thoroughly assessed in comparison to that of each of the base models and alternative ensemble approaches. Evaluation metrics validate BatOptiStroke's stroke prediction capabilities. The combined model set consistently outperforms individual base models, improving prediction accuracy and overall performance. Along with increased 97% accuracy, 89% precision, 95% recall, and a 93% F1 score, BatOptiStroke also contributes.
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