## 1 Introduction  
Biosensor-based Internet of Things (IoT) devices combined with intelligent wireless technology and data-mining techniques lead to a better healthcare system for investigating information using interactive patient monitoring methods. This new interactive healthcare system will gather information from a patient within a short period of time using biosensors and sensor-less devices. Researchers believe that the collection of medical information is one of the major parts of healthcare research [1]. Human-Computer Interaction (HCI) is therefore crucial for gathering a greater amount of patient information with less effort and less time to analyze. A huge amount of ongoing data is produced by the healthcare system, and comes in unstructured or semi-structured format. To store this information, current healthcare systems use electronic health records (EHRs). As indicated by the data analysis of healthcare, US healthcare produced 150 exabytes of medical data in 2011 [2]. That amount had reached the zettabytes in 2014. It is necessary to negotiate an interactive healthcare system with this big amount of medical data to uncover hidden patterns, and search for unrevealed correlations from prior medical information of patients. The main task in medical data analytics is to address the characteristics of big data identified by the 5 Vs: volume, velocity, variety, value, and veracity. In this new interactive healthcare system, patient data comes from various sources, describing the volume of data. The arrival rate of data represents the velocity of data. The doctor's note and the other clinical reports come from numerous sources, and those data come in structured, semi-structured, or unstructured format, which describes the variety. Those data need to be analyzed to reveal meaningful information, which is considered to be the value of data. There are many uncertain numbers,in the data which we call hidden data, and they represent the veracity. Some advanced technologies, such as tensors, cloud computing, and some intellectual frameworks, are used to analyze big data [3; 4; 5; 6; 7; 8]. Big data processing, however, leads to problems, as much semi-structured and unstructured information exists. International Business Machines (IBM) healthcare researchers found that 80% of big data in healthcare is unstructured. They also claimed to recover helpful patient information and rendered these data in a structured format, and these comprehensive unorganized data need to be examined [9]. As digital data has proliferated, IBM scientists have compared the volume of the last decade's structured and unstructured data. According to International Data Corporation (IDC) research, among all the complete incremental digital data, almost 90% of the data are unstructured [10]. The unstructured and semi-structured dataset may contain appropriate data information, and this is the reason for providing inaccurate dataset information. An interactive healthcare system needs to provide a user-friendly environment for data analysis. HCI in data analytics can activate data visualization with the help of new technologies. Data analytics is indispensable for user understanding, and HCI in data analytics can facilitate that efficiently and accurately. Advanced HCI solutions generate not only confidential data but also represent data analytics result more efficiently. To promote the diagnostic impact, HCI in data analytics enables visualization of the graphical perspective of data. From all the medical data of a patient using big data analytics and HCI, the interactive healthcare system must predict the disease of the patient and should predict probable illnesses that a patient may suffer in the future. The predictive facility should also be available for the interactive healthcare. To accomplish this objective, a prediction algorithm could be used. There is a powerful connection between the model of prediction and the quality of medical data.