Healthcare data modeling in R

• Published in: 2017 1st International Conference on Intelligent Systems and Information Management (ICISIM) pp. 230 - 233
• Main Authors: Pant, Diva, Kumar, Vishal, Kishore, Jaydeep, Pal, Ritu
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-10-2017
• Subjects: classification; Classification algorithms; Data mining; Decision trees; Diseases; EHR; healthcare analytics; Hospitals; Object oriented modeling; pattern recognition
• DOI: 10.1109/ICISIM.2017.8122178

The unprecedented interest in big data has paved way for augmented technologies. One of the major usefulness of big data is found in the field of healthcare analytics. The healthcare data come from varied sources. Specifically EHR data provide a comprehensive view of patient's health. People are paying more attention to their health and want the best possible healthcare especially with new technologies evolving every now and then. We can analyze this astronomical patient's information and try to study certain patterns, which can give us the better understanding of the data present. In this study a neurological dataset of thousand patients has been collected from a hospital. Out of this data the particular cases of head injury are taken into account and specific attributes like pulse rate, blood pressure, Glasgow coma scale, respiratory rate, CNS are studied and analyzed. The analysis is performed on the basis of two factors: duration of patient's stay in the hospital and seriousness level of the injury. A classification model is prepared on the data and the implementation is carried out in R Programming, using its statistical packages and graphical abilities.