## 3 Results  
SCADI datasets face two problems: unbalanced datasets, and high dimensional features that lead to inaccurate classification. We trained several ML classification algorithms \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Methodology** & **Key Techniques** & **Best Model** & **Accuracy** \\ \hline \multirow{2}{*}{[PERSON] et al. [15]} & \multirow{2}{*}{ANN, DT} & 205 neurons in the input layer, 40 neurons in the & \multirow{2}{*}{ANN} & \multirow{2}{*}{83\%} \\  & & 235 hidden layer & & \\ \hline \multirow{2}{*}{[PERSON] et al. [16]} & ELM, KNN, SVM, & \multirow{2}{*}{Principal Component Analysis (PCA)} & \multirow{2}{*}{KNN} & \multirow{2}{*}{84\%} \\  & ANN, RF, GB & & \\ \hline \multirow{2}{*}{[PERSON] et al. [18]} & \multirow{2}{*}{FNN, C.45,MLP,SVM} & \multirow{2}{*}{Artificial neural networks and fuzzy systems.} & \multirow{2}{*}{FNN} & \multirow{2}{*}{85\%} \\ \hline \multirow{2}{*}{[PERSON] [20]} & \multirow{2}{*}{DT, DNN} & Autoencoders and deep neural networks & DNN & 81\% \\ \hline \multirow{2}{*}{[PERSON] et al. [21]} & \multirow{2}{*}{RF} & Correlation-Base Feature Selection, Fast fourier transform and inverse fast fourier transform & \multirow{2}{*}{RF} & \multirow{2}{*}{84\%} \\ \hline \multirow{3}{*}{[PERSON] [22]} & \multirow{3}{*}{Advagost, DT, Bagging, Extra Tree, GB, RF, Hist.GB} & \multirow{3}{*}{Adaptive Boost Algorithm with Decision Tree} & \multirow{2}{*}{E-ADAD} & \multirow{2}{*}{85\%} \\ \hline \multirow{2}{*}{Proposed Model} & \multirow{2}{*}{MI-RFE} & Resampling, Data Shuffling, Hyper-framework & \multirow{2}{*}{RF} & \multirow{2}{*}{99\%} \\  & & Feature Selection (MI, RF-RFE), SHAP & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative Analysis of Methodological Settings. using a novel preprocessing approach. In the first step, the ROST was used to solve the imbalance issue in datasets I and II, increasing the minority class range by creating additional synthetic samples. In the second stage, a two-phase feature selection technique was used: filter (MI) method and wrapper (RFE) method, respectively. MI is an effective filtering method to remove irrelevant features. Also, RF was assigned as the RFE estimator because of its speed and efficiency. In this work, four ML approaches were used to evaluate whether patients had a disability: SVM, DT, bagging, and RF. The evaluation was performed using the 10-fold cross-validation method, which is used in ML models to avoid data overfitting. All features were normalized before being applied to classifiers. Table 2 shows the performance of the binary class dataset's original and preprocessing results. We employed accuracy, precision, recall, f1-score, and kappa, with 10-fold cross-validation score as performance metrics. In this work, we employed oversampling to overcome the imbalance problem in the datasets and then used hyper MI-RFE feature selection due to the high feature space in the SCADI dataset may be decreased by utilizing filter-based (MI) guards to enhance the time complexity of the wrapping approach (RFE). In the evaluation metrics for the original binary class dataset, which comprises 206 features and 70 patient records, SVM was the second-best model, with an accuracy of 88.57%. After applying resampling and MI feature selection, the total patient records reached 108 patients and 80 features, and the accuracy of RF increased to 97.1% selected as the best model. Bagging reached 95.47% accuracy and was selected as the second best model. The reason for this increase is that feature selection can decrease data noise and choose the most important features to employ during the training process. RF-RFE feature selection was applied as the second feature selection, as the optimal feature obtained from MI was used and fed into the RFE to reach the best optimal features that affect the prediction of the ICF-CY dataset based on the best accuracy features obtained from the random forest classifier. The optimal features obtained from RFE are 14 features.