A-Tuning Ensemble Machine Learning Technique for Cerebral Stroke Prediction

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.

Predictive modelling and identification of key risk factors for stroke using machine learning

“…Additionally, Random Forest is studied 32 – 34 , 36 with accuracies ranging from 95.50% to 96.00%. The proposed RXLM 35 model achieves an accuracy of 96.34% on the balanced dataset. K-nearest Neighbours 37 model achieves accuracies of 94.00% on the balanced dataset.…”

“…Although there are some limitations in the proposed work, such as only using two models are used however it provides valuable insight into stroke prediction research. A recent study suggests an ensemble RXLM model to predict stroke using Random Forest, XGBoost, and LightGBM 35 . The dataset is pre-processed using the KNN imputer technique, one-hot encoding, and SMOTE.…”