# A Fair and Safe Usage Drug Recommendation System in Medical Emergencies by a Stacked ANN
## 5 Discussion  
For example, a DRS for migraine patients can help doctors write the appropriate and accurate drugs for the patients based on their severity and importance [44]. The proposed DRS consists of individuals who are 80% similar to the present diseased individual. An additional system for diabetic patients using collaborative filtering can recognize patients that closely match the active patient by considering features such as insulin, glucose, body mass index, and blood pressure [45]. By using information about a patient's profile, combined with ontologies and rule-based decision making, these systems can recommend anti-diabetes medicines with dose restrictions [46]. A patient with COVID-19 and susceptible virus-related fatigue was treated with chloroquine, as reported in [47]. Lopinavir significantly decreased the incidence of ARDS in patients with SARS-CoV-2 infection [48]. Nafamostat has been reported as a treatment for pancreatitis and abnormal coagulation that occur frequently in COVID-19 patients [49]. In the early phases of the COVID-19 treatment and for the treatment of influenza, camostat is used [50]. Gastric ulcers are treated with farotime, which has few side effects and an adequate efficacy and reduces iNOS activity [51]. With its immune system boosting effects, Nitroxanide improves the respiratory distress associated with SARS-CoV-2 [52]. Combined with other COVID-19 treatments,vermectin lowers mortality rates and hospital stays for moderate COVID-19 patients [53]. Researchers used resilient distributed dataset programming to implement the density peak-based clustering algorithm in their study [54]. A system for identifying sickness and treatment association rules was proposed to identify disease diagnosis recommendations. Unfortunately, the outbreak of coronavirus has limited the availability of legitimate clinical resources, such as doctors, nurses, and equipment. Due to the distress of the medical profession, a lot of people are dying. [PERSON] et al. [55] outlined a system for performing infectious disease diagnoses and clinical decisions. In the proposed system, antibiotic usage is recommended using the naive Bayes classifier, and the ontological relations and rules are accurately stored in Neo4j. Fairness is the major bias arising from recommender systems. Certain characteristics, such as race, gender, age, qualification, or property, are not represented equally in the dataset. In the case of unbalanced data, it is possible to highlight overrepresented groups in the rankings while reducing visibility for underrepresented groups. According to previous studies, the feedback loop causes the high usage medicines to become more popular and the low usage medicines to become less popular. Using the Type 2 fuzzy ontology and the wearable sensors for diabetes treatments, another study has reported that drugs and diet plans are available to patients [56]. Based on the findings of the authors, the proposed system is effective in extracting diabetic patients' risk factors and recommending drug therapy. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Recommender Model** & **Accuracy** & **Precision** & **Sensitivity** & **Specificity** \\ \hline Content-Based & 0.847 & 0.842 & 0.862 & 0.897 \\ \hline Hybrid restricted Boltzmann machine & 0.946 & 0.932 & 0.926 & 0.927 \\ \hline Random forest & 0.841 & 0.840 & 0.841 & 0.920 \\ \hline K nearest neighbors & 0.840 & 0.823 & 0.824 & 0.915 \\ \hline Support vector machine & 0.719 & 0.714 & 0.719 & 0.860 \\ \hline Logistic regression & 0.534 & 0.522 & 0.534 & 0.767 \\ \hline Decision Tree & 0.840 & 0.840 & 0.835 & 0.920 \\ \hline DeerDr [35] & 0.956 & 0.945 & 0.924 & 0.919 \\ \hline MLP [36] & 0.946 & 0.945 & 0.917 & 0.916 \\ \hline Proposed & 0.985 & 0.96 & 0.939 & 0.929 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the performance metrics of machine learning-based recommender systems.