# A-Tuning Ensemble Machine Learning Technique for Cerebral Stroke Prediction
## 2 Literature Review  
As presented in [14], different machine learning techniques can predict a patient with a high stroke risk. The research will use three models of machine learning algorithms: RF, DT, and NB. After evaluating each approach, the prediction uses the patient's health history as the attribute in each mode. The RF method has the highest accuracy, at 94.781%, followed by the DT method, which recorded an accuracy of 91.906%, and the NB method recorded the lowest accuracy, at 89.976%. The RF method outperforms the other methods in terms of accuracy based on these findings. Using a variety of elements that record the participant profiles, the authors of [15] investigated the efficacy of various ML algorithms, RF, LR, K-NNs, SGD, DT, stacking, and majority voting, to identify the accurate algorithm to predict the cases of stroke. Based on the results that exceed 98%, stacking classification obtains high results. As a result, the stacking technique effectively identifies those who are eventually likely to suffer a stroke. The authors of [16] trained four distinct models for accurate stroke prediction with machine learning algorithms and numerous physiological parameters. With an accuracy of approximately 96%, the RF performed the best for this task. As proposed in [17], the authors used two datasets. The first dataset is a list of medical checks, changes in the environment, and changes in the body. The dataset was balanced using the SMOTE method, missing values were replaced with KNN Imputer values, and outliers were removed. According to the corresponding features values, diabetes and obesity were created as two features. The five ML algorithms that ultimately fed these features were SVM, KNN, DT, RF, and MLP. The MRI scans comprise the second dataset, where the average filter has been used to optimize each image and eliminate noise. The dataset has been balanced using a data augmentation technique to prevent overfitting of data. Relatively 80% of the dataset was used for training, validation, and 20% for testing. The AlexNet model and SVM algorithm were used to extract the deep features. The deep learning model (AlexNet) performed worse than the hybrid algorithm that combined machine learning and deep learning. AlexNet and the SVM achieve 99.9% accuracy, 100% sensitivity, 99.80% specificity, and 99.86% AUC, respectively. The authors of [18] demonstrate how several machine-learning systems may correctly predict stroke cases based on physiological parameters. The NB Classification exceeds the alternative algorithms with an accuracy of 82%. As explained in [19], the authors proposed a comprehensive assessment of patient characteristics in the digital health record. They used systematic analysis to look at various features. They carried out feature correlation and stepwise analysis to select the best features. In addition, they performed a principal component analysis (PCA), and the results explained that the principal components are required to account for more variance. Finally, various features and principal component configurations were used to test three machine-learning algorithms, neural network (NN), DT, and RF. They discovered that a feature combination of A, HD, HT, and AG works best for a NN, with an accuracy and miss rate of 78% and 19%, respectively. As presented in [20], the authors used a stroke prediction algorithm and an improvised RF ensemble technique to find the risk factor. The prediction model has a 0.03% of error rate and an accuracy of 96.97%. As presented in [21], the authors proposed a hybrid framework to predict cerebral stroke disease using classification and clustering. The authors implemented clustering using an enhanced hierarchal clustering technique and then implemented LR, RF, SVM, NN, and XGBoost classifiers. According to accuracy and AUC, all the classifiers produced satisfactory results, and the RF classifier achieved 97%, which was the best result. We can conclude from the above literature review of the most recent research that none accurately classified cerebral stroke.