## 2 Background and Related Work
### Feature-Based Methods  
Feature selection has been used previously for improving the classification performance in different medical situations. Particularly, [PERSON] et al. have proved that by performing feature selection, the results of the classifier can be improved for the prediction of success or failure in Noninvasive Mechanical Ventilation (NIMV) in Intensive Care Units (ICU) [35]. Similarly, [PERSON] [36] used F-score feature selection-based SVM model, and [PERSON] et al. [37] used SVM with rough-set based feature selection for the improved diagnosis of breast cancer. [PERSON] et al. [38] performed a premier study on developing deep learning-based classifiers for atrial fibrillation. They built six models based on feature-based approaches and DL approaches. However, their features are manually extracted while the DL methods are trained on raw data without any feature engineering, as they perform implicit feature selection. It is unclear how they performed the manual feature extraction, but manual feature extraction is not a preferred approach if this can be done automatically, as explained later in our case. [PERSON] et al. applied a feature engineering approach to gain clinical insight and, thus, improve the ICU mortality prediction in field conditions [39]. The authors used only linear hard margin SVM as it maximizes the separation between different classes. Feature selection was performed using statistical and dynamic feature extraction with an evaluation performed after each step. Any misclassifications after these two stages were investigated manually. A final phase of feature fine-tuning consists of seven steps and utilizes the vital signs as opposed to the selection of dimensions in the previous stages. Results were then obtained by evaluating the various combinations of feature selection performed in different stages. The interesting aspect of their approach is the combination of different features at different stages and improving the results step-by-step. A conclusion of the work was that different profiles of patients required a different set of features for efficiently predicting the mortality of patients. [PERSON] and [PERSON] used the hybrid feature selection technique [40] on three different datasets of diabetes, hepatitis, and breast cancer. Their model adopted Weighted Least Squares Twin SVM (WLSTSVM) as a classification approach, sequential forward selection as a search strategy, and correlation feature selection to evaluate the importance of each feature. In contrast, we applied permutation importance for feature selection, which is known to be a faster technique without the need for a selection strategy. Once the features were found, we could use any of the ML models to classify and predict the data. Specific for the case of diabetes, [PERSON] et al. used SVM ranking with backward search for feature selection in T2D databases [41], where they proposed a specific feature selection approach for finding an optimum feature subset that enhanced the classification accuracy of Naive Bayes classifier. [PERSON] [42] constructed a combined model using genetic algorithms and fuzzy logic for future subset selection. However, genetic algorithms have their own associated costs, and the proposed approach did not justify the cost compared to the achieved accuracy. On the same lines, [PERSON] et al. [43] used genetic programming with Pima Indian diabetes dataset by generating subsets of original features by adding features one by one in each subset using the sequential forward selection method. The approach is not only costly but their results using 10-fold cross validation with KNN(K-Nearest Neighbor) and a specific configuration of genetic programming yielded an accuracy of about 80.5%, which is not up to par with other contemporary approaches. [PERSON] et al. [44] applied feature selection on T1D Mellitus (T1D) patients using variables like sleep, schedule, meal, exercise, insulin, and heart-rate.