Hash polynomial two factor decision tree using IoT for smart health care scheduling. (1st March 2020) - Manikandan, Ramachandran; Patan, Rizwan; Gandomi, Amir H.; Sivanesan, Perumal; Kalyanaraman, Hariharan
Highlights: Towards improvement of the smart health care facility, introduced the smart scheduling. Proposed a Hash Polynomial Two-factor Decision Tree technique. It achieves by classifying patients as being normal or critical state in minimal time. To increases the scheduling efficiency and reduce the response time. Data collection is performed by using the Polynomial Data Collection (PDC) algorithms. Abstract: The steady growth of an aging population and increased frequency of chronic disease led to the development of Smart Health Care (SHC) systems. While patient prioritization is the core of any SHC system, handling the response time by medical practitioners is a prevailing challenge. With advancements in information technology, the concept of the Internet of Things (IoT) has made it possible to integrate SHC systems with the Cloud environment to not only ensure patient prioritization according to disease prevalence, but also to minimize response time. In this work, an IoT-based scheduling method, called the Hash Polynomial Two-factor Decision Tree (HP-TDT) is proposed to increase scheduling efficiency and reduce response time by classifying patients as being normal or in a critical state in minimal time. The HP-TDT scheduling method involves three stages including the registration stage, the data collection stage, and the scheduling stage. The registration phase is carried out through Open Address Hashing (OAH) model for reducing the key generation response time. Next, the data collection stage is performed using the Polynomial Data Collection (PDC) algorithm. By incorporating PDC, computation overhead is reduced because a number of operations are considered during data collection. Finally, scheduling is performed by applying two-factor, entropy and information gain according to a decision tree. With this, scheduling efficiency is improved due to the classification of patients as being normal or in a critical state. The proposed method minimizes response time, computational overhead, and improves essential scheduling efficiency.