## 2 The State-of-Art  
The healthcare industry has been continually improving over the past half-decade, using sophisticated techniques to keep track of patient data. However, a more advanced medical system is being developed by scientists. Many scientists have recently suggested several alternatives to enhance the quality of healthcare. Among those system, \"BDAEH\" provides a potential method to enhance the service of the medical healthcare system. This suggested technique addresses the appropriate physical and emotional information of the patient and then analyzes the data to provide better medical facilities [11]. \"BDAEH\", however, neglects two key variables: a patient's medical history and their genetic information. \"IHEMHA\", another suggested healthcare system, is concerned with these significant variables and shows the importance of prediction analysis [12]. While researchers are developing the interaction between the healthcare industry and the patient, some have also clarified the significance of important data analysis. They outlined that medical data could provide healthcare organizations with future possibilities. Researchers have also prioritized the importance of analyzing big data in healthcare as data increases in the time domain [13; 14; 15]. There are still some techniques suggested that could improve a medical system's interaction and improve the healthcare industry's service. The EHR is such a solution that can gather data from the patient and analyze it. For the development of a diagnostic model, some other healthcare decision support systems have been suggested, applying some machine-learning algorithms to medical data [16; 17]. While these models provide helpful techniques for enhancing the healthcare service, a drawback of these schemes is the data storage capacity that the patients generate. Medical images, and signal and genomic data are another important information source for big medical data. These data also need to be processed and analyzed as they also have [18]. Researchers are currently researching an interactive solution for the storage of medical data [19; 20; 21]. Some experts suggest not only using a cloud server for big data analysis, but also providing rapid computing with prediction analysis, which plays an important role in the various clinical circumstances [22; 23]. An additional concern, while dealing with big data from the database, is to handle the missing attributes. According to academics, the Markov model could be a solution to handle missing data [3]. Researchers have shown that medical data are being collected from the patient continuously, which will lead to the storage of a significant amount of data, and could be difficult to handle using the existing technology. The reason for this is that those collected data are somewhat unstructured, semi-structured, or sometimes structured. We need to use a structured database before evaluating these data to prevent unnecessary errors. MapReduce would be effective in converting the unstructured and semi-structured dataset to the structured dataset. MapReduce algorithm can handle the huge quantity of data and can faster transform these data into a structured format [1; 24]. The emotional state of a patient is another critical factor in detecting disease. Emotion is a complex method that involves facial expression and responses, intuition and behavior. Scientists have studied the feeling of a patient. The Emotion API is one of the pieces of research on emotion detection that can identify a person's emotional state from images and videos [25]. There has been some research to classify emotion using Heart Rate Variability (HRV) features [26]. The HRV features deal with multiple signals that a patient produces in order to extract the emotional state.