A-Tuning Ensemble Machine Learning Technique for Cerebral Stroke Prediction

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A-Tuning Ensemble Machine Learning Technique for Cerebral Stroke Prediction
by Meshrif Alruily 1,Sameh Abd El-Ghany 2ORCID,Ayman Mohamed Mostafa 2,*ORCID,Mohamed Ezz 1ORCID andA. A. Abd El-Aziz 2ORCID
1
Computer Science Department, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Aljouf, Saudi Arabia
2
Information Systems Department, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Aljouf, Saudi Arabia
*
Author to whom correspondence should be addressed.
Appl. Sci. 2023, 13(8), 5047; https://doi.org/10.3390/app13085047
Submission received: 30 March 2023 / Revised: 15 April 2023 / Accepted: 16 April 2023 / Published: 18 April 2023
(This article belongs to the Special Issue AI Technologies for eHealth and mHealth)
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Abstract
A cerebral stroke is a medical problem that occurs when the blood flowing to a section of the brain is suddenly cut off, causing damage to the brain. Brain cells gradually die because of interruptions in blood supply and other nutrients to the brain, resulting in disabilities, depending on the affected region. Early recognition and detection of symptoms can aid in the rapid treatment of strokes and result in better health by reducing the severity of a stroke episode. In this paper, the Random Forest (RF), Extreme Gradient Boosting (XGBoost), and light gradient-boosting machine (LightGBM) were used as machine learning (ML) algorithms for predicting the likelihood of a cerebral stroke by applying an open-access stroke prediction dataset. The stroke prediction dataset was pre-processed by handling missing values using the KNN imputer technique, eliminating outliers, applying the one-hot encoding method, and normalizing the features with different ranges of values. After data splitting, synthetic minority oversampling (SMO) was applied to balance the stroke samples and no-stroke classes. Furthermore, to fine-tune the hyper-parameters of the ML algorithm, we employed a random search technique that could achieve the best parameter values. After applying the tuning process, we stacked the parameters to a tuning ensemble RXLM that was analyzed and compared with traditional classifiers. The performance metrics after tuning the hyper-parameters achieved promising results with all ML algorithms.