## 6 Results and Discussions
The random forest model had the lowest standard deviation of 0.11% in accuracy across cross-validation folds. All models except Naive Bayes showed a mean accuracy of more than 80%. Figure 7: Cross Validation Accuracy of Each Fold for All ML Algorithms. A confusion matrix represents the prediction summary in matrix form. It consists of four different combinations of predicted and actual values, as can be seen in Figure 8. Statistical measures like accuracy, sensitivity, specificity, precision, and F1 score were derived from the matrix as performance metrics to derive the model's effectiveness and performance. A short definition of the performance metrics with their calculating formula is given below:  
* Sensitivity: The ability of a machine learning model to predict true positives. \[\text{Sensitivity}=\frac{TP}{TP+FN}\] (1)
* Specificity: It describes how well a classifier can detect the true negatives. \[\text{Specificity}=\frac{TN}{TN+FP}\] (2)
* Accuracy: Accuracy can describe a model's capability to do accurate prediction overall predictions. \[\text{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN}\] (3)
* Precision: It is the ability of a classifier to identify what proportion of positive results are actually positive. \[\text{Precision}=\frac{TP}{TP+FP}\] (4)
* F1-Score: F1-Score measures a model's accuracy on a dataset. \[\text{F1-Score}=\frac{2 TP}{2 TP+FP+FN}\] (5)  
With these matrices, we made a performance comparison of all the models to find out the best. Evaluation of all the detection models on neurological complications showed us accuracy in the range of 80-86%. Among the classification techniques, the best accuracy was shown by the ANN algorithm, with 86.62%. The model has a sensitivity of 88.17% and a specificity of 85.06%. The next best accuracy of 85.85% was achieved by Extreme gradient boosting. Random forest algorithm, Logistic regression and Support vector machine showed a similar performance with an accuracy of around 85%. All models showed an accuracy of over 80% while Naive Bayes had the lowest of 80.65%. The detailed performance of all the algorithms is shown in Figure 9.  
Figure 8: Confusion Matrix. The XGBoost model yielded the best sensitivity of 88.21% followed by ANN with 88.17%. NB model showed a specificity of 91.14% and precision of 88.78% which is better than other models while the sensitivity was quite poor--70.16%. ANN has again shown the best performance in terms of F1 score--86.82%. The performance of the XGB model was quite comparable to ANN. It achieved an accuracy of 85.85% with sensitivity and specificity of 88.21% and 83.49% respectively. A visual representation of the performance for all the models is depicted in Figure 7:0  
To find the best-performing algorithms, we cannot just rely on the evaluation matrices. Because for any imbalanced data set, the model will always predict the majority class which is unexpected. A receiver operating characteristic (ROC) curve can be used to assess if a dataset has such critical points. ROC curve is a graph representation of a classification model at various classification thresholds, while Area-under-Curve (AUC) tells us the  
Figure 7:1 Performance Comparison of the AI Models on the Selected Evaluation Criteria. Figure 7:2 Performance of Detection Models. degree of measure of separability between binary classes. The two parameters used to draw the curve are: the false positive rate and the true positive rate of predicted classes. Generally, ROC-AUC curves can judge the discrimination ability of various statistical methods that combine various clues, test results, etc. for predictive purposes [42]. The value of AUC indicates the classification performance of a model. When it is 1, it means the classifier can correctly distinguish between all the patients and the non-patients. A value of 0 indicates that the classifier would predict all the non-patients as patients and the patients as non-patients.