## 6 Results and Discussions
Below in Figure 11, an ROC-AUC curve is drawn to summarize the performance of all the models depicting the ability of each model in the detection of positive cases. The ROC-AUC curves for all the models are almost entangled with each other as their performances are quite similar. The red solid line in the curve is representing the AUC score of the ANN model, which has a slight edge over other models. It is because the model has the highest AUC score of 0.924 which indicates that the ANN model has the best ability to detect true positive cases. The AUC score of the support vector model is the lowest--0.890 as depicted with the blue dash-dot line, but the value is quite close to ANN's score and surely can demonstrate comparative performance in the detection of true positive cases. An important observation we made from all the classification models is: not all the features among the selected 8 are significant. Their importance and ranking are varied from model to model. We used the 'Feature Importance Score' to identify how each feature makes an impact on the detection/classification accuracy. A Scikit-learn library 'Permutation_Importance' was implemented in our algorithm to assign a score for all the input features for a given model, where the scores simply represent the \"importance\" of each feature. A higher score indicates that the specific feature will have a larger impact on the model for classifying patients and non-patients. In Figure 12, feature importance scores of all the models are depicted for better understanding. Figure 7:3 ROC-AUC curve to compare ML models’ performance. The observation from the above figure states that almost every model has their own feature importance ranking. Based on our best-performing model ANN, the three most important features are--'Neuro Abnormality', 'Communication Disturbance' and 'Trauma', but for the XGB model, 'Pain or Discomfort at head' is the third most important feature instead of 'Trauma'. 'Communication disturbance' and 'Neuro Abnormality'--seem to be the most important two features in all the models except the Naive Bayes model. 'Communication Disturbance', 'Brain Injury' and 'Trauma' are considered the most important features in all the models except the Naive Bayes model. features of this model. The results pointed out how important it is to implement NLP to extract new features from the text data of the rescue database. All this is generally recorded by the rescuers at emergency sites from their personal observation. Our models suggest that the first observation rescuers need to perform for detecting neurological complications is to find if the patient has any impairments in vision, speech, or movement. Other signs to be observed are: the patient's choice of words, capability to control the body and overall responsiveness. As for neurological abnormalities, rescuers need to check signs of seizure attack, the shape of the mouth, tongue bite, uncontrolled urination, etc. in patients. It is also important to take note of historical records of patients like if the patient had any trauma or brain injury recently, or whether he or she has any past record of neurological complications like epilepsy or brain tumor. Such observations should be given utter importance and passed to the machine learning-based system as data immediately for quick diagnosis. In our findings, GCS score did not make a high impact in any of the models. One reason could be that, due to the nature of rescue situations, it is quite difficult to perform such a test or record the score correctly. Another goal of our research was to calculate the inference time for each model with test data to understand if the model will be time efficient in real-world scenarios. We did the calculation by passing a single test data to each of the models and then record the time of detection. The result can be seen in Figure 7:4  
The table showed that the FPGA accelerated ANN model with Apache TVM-VTA had an inference time of only 13 ms and was actually faster than the deep learning model compiled with a computer CPU.