## 5 System Evaluation  
The proposed technique is evaluated and compared with existing classical and advanced machine-learning models. In classical models, we targeted zeroR, Naive Bayes, J48, and Random Forest as shown in Figure 8a, while in advanced models, our focused algorithms are neural network (CNN), long short-term memory (LSTM) and Bi-directional LSTM (Bi-LSTM) as shown in Figure 8b. In classical models, ZeroR achieved 69%, Naive Bayes 69%, J48 67%, and Random Forest achieved an accuracy of 67% on asthma guideline; however, the proposed approach achieved higher accuracy of 78.89%. Similarly, the accuracies of these algorithms on Rhinosinusitig guideline were, 80%, 80%, 81%, 84%, respectively, while the proposed technique performed better with accuracy of 85.32%. Likewise, the proposed algorithm correctly classified Hypertension CPG sentence with an accuracy of 90.07%, which is higher than all classical models as depicted in Figure 8a. The improved results of the proposed methodology are mainly due to the relevant patterns exercation, by combining expert heuristics with machine-learning techniques, and the generalization of the patterns through POS, and UMLS techniques. Figure 7: Combined patterns accuracy (**a**) without salient terms (**b**) with salient Terms. In advanced models, the accuracy of CNN is 72.72%, LSTM is 65.90%, Bi-LSTM is 68.82%, and the proposed system is 78.89% on asthma guideline. On Rhinosinusitis CPG, the accuracies were 84.38%, 81.15%, 84.04%, and 85.32%, respectively. However, in the Hypertension guideline, our proposed approach showed better results than the advance machine-learning models, which is 90.07% higher than 71.42%, 74.29, and 77.14% as shown in Figure 8b. The results obtained from the deep-learning models surpassed the classical models in terms of accuracy. However, the proposed technique performed better than deep-learning models. This is mainly because deep-learning models are data-hungry models and required a large training data than the provided one. The datasets used in the study have a small number of sentences, and the distribution between recommendation and non-recommendation sentences is also very biased towards non-recommendation. Therefore, data-hungry models such as deep-learning models did not perform well as shown in Figure 8b. To overcome this deficiency, we checked the applications of these advanced models with a large dataset by bootstrapping our dataset. Three different experiments using bootstrapping and data balancing techniques were performed and the results obtained are shown in Figure 9.  
Initially, we merged all three datasets given in Table 8 resulted in a comparatively large and an imbalanced dataset of 1210 sentences with 282 recommendation and 928 non-recommendation sentences. We named the generated dataset as \"Merged Data\". The application of classical and advanced machine-learning models on this dataset is shown in Figure 9,b, respectively. Among the classical model, decision tree (J48) model performed the best at an accuracy of 77.19%, but still below the proposed technique which stands at 81.63%. In deep-learning models CNN achieved 77.69%, LSTM 76.86%, and Bi-LSTM surpassed the proposed technique by 0.39%. The merged dataset is more inclined toward non-recommendation sentences, therefore, the trained models are also biased toward the non-recommendation sentence. We overcome dataset biases by duplicating the number of RS sentences, and swap theirs tokens, repeatedly. The resultant dataset referred to as \"Swap Data\" in Figure 9 consist of 846 RS and 929 NRS of 1775 sentences. The evaluation results of classical and deep-learning models on Swap Data are reflected in Figure 9, where the Naive Bayes achieved the highest accuracy of 76.95% in classical models while Bi-LSTM achieved highest accuracy of 79.88% in deep-learning model compared to 77.61% accuracy of the proposed technique. Duplicating instances and swapping tokens may not be an efficient approach for trained a generalized model.