The increasing usage of smartphones in daily life has received considerable attention in academic and industry driven research to be utilized in the health sector. There has been development of a variety of health-related smartphone applications. Currently, however, there are few to none applications based on nurses' historical or behavioral preferences. Mobile application development for the health care sector requires extensive attention to security, reliability, and accuracy. In nursing applications, the users are often required to navigate in hospital environments, select patients to support, read the patient history and set action points to assist the patient during their shift. Finally, they have to report their performance on patient related activities and other relevant information before they leave for the day. In a working day, a nurse often visits different locations such as the patient's room, different laboratories, and offices for filling reports. There is still a limited capability to access context relevant information on a smartphone with minimal recourse such as Wi-Fi triangulation. The Wi-Fi triangulation signals fluctuate significantly for indoor location positioning. Therefore, providing relevant location based services to a mobile subscriber has become challenging. This paper addresses this gap by applying machine learning and behavior analysis to anticipate the potential location of the nurse and provide the required services. The application concept was already presented at the IMCOM 2015 conference. This paper focuses on the process to ascertain a user's context, the process of analyzing and predicting user behavior, and finally, the process of displaying the information through a dynamically generated UI.