# A Fair and Safe Usage Drug Recommendation System in Medical Emergencies by a Stacked ANN
## 1 Introduction  
Online consultations require the patient to describe their symptoms to the doctor. A spike in virtual medical services has been reported in the wake of the novel coronavirus disease (COVID-19) [1]. Diabetes, hypertension, and heart disease are all associated with an increased risk of virus infections. The availability of health care professionals 24/7, no need for travel, security, privacy, and drug recommendations are all advantages of virtual medical services. The recommender system allows for improvements in medical services in disparate areas [2]. Often, finding a physician in remote areas can be tricky, so recommender systems have been created to help.  
Health-related recommender systems can make an early diagnosis, predict disease progression, and make appropriate recommendations according to the health status of patients [3, 4]. Machine learning (ML) greatly improves the quality of medical recommender systems by providing suggestions that are based on patient needs and feedback [5, 6]. By using sentiment analysis and feature engineering, the drug recommender system can dispense medicine according to a specific condition. Emotions, such as attitudes and opinions, are separated and extracted from language through sentiment analysis [7].  
By using the recommender system, information overload can be solved, and e-government and e-learning can be improved [8]. Depending on an individual's health status, these recommender systems prescribe medications, diagnose diseases, and refer them to the relevant health care. An ML-driven recommendation system generates appropriate recommendations using parameters such as blood pressure, gender, cholesterollevels, and blood sugar for diseases such as colds, fevers, and cardiac deaths [9]. The healthcare system built on the Internet of Things (IoT) coupled with an oncology interface has provided nutrition information to individuals [10].  
Depending on the patient's medical history, a decision support system can assist a doctor in prescribing a drug. In contrast, the recommendation system suggests the same based on an analysis of previous usage patterns [11]. Four types of recommender systems exist, including content-driven filtering, collaborative filtering, knowledge-driven recommender systems, and hybrid recommender systems [12; 13]. Since the drug recommendation framework includes medical terminology, such as infection names, side effects, and synthetic names, only a limited number of papers are available.  
In this work, we proposed the development of a drug recommender system (DRS) for different diseases to maintain good patient health and longevity. We addressed the unfairness in drug usage by DRS for severe chronic diseases by improving the recommendation accuracy by the integration of ML knowledge. A further discussion was carried out on how the proposed DRS integrates the person's health profile and automation of meditation, and drug dosages. The system performance was calculated based on different metrics such as accuracy, sensitivity, and specificity. The performance was further compared with other existing ML models to validate its efficiency.