IoT Enabled Data Classification Framework for Healthcare Based on Fog Computing

  • Amit Kishor, Wilson Jeberson


In growth of population, chronic disease is increasing with respect to the density of population and it amplified expenses in medical care. Thus, the technological enhancement must be adopted in healthcare domain to get better improvement in patient’s health. In this perspective, the improvement in monitoring of patient’s risk can become prevalent by using world’s most vital technology wireless sensor network. However, use of technology based on cloud computing can create delays became blunder for patient’s health. Now fog computing concepts come between sensor and cloud computing to overcome the issue. Motivated from this, we propose a random forest (RF) learning algorithm for patient’s data classification with low latency. The aim of this paper is to analyze and monitor the patient’s health data for real-time data collection. In order to achieve this goal, the training of RF model is done and is to be map with collected sensors data to produce the output data as normal, less sensitive and high sensitive. As a result, it reduces the excess movement of patient’s to healthcare agencies and timely patient’s can contact to healthcare in emergency conditions.

How to Cite
Amit Kishor, Wilson Jeberson. (2020). IoT Enabled Data Classification Framework for Healthcare Based on Fog Computing. International Journal of Advanced Science and Technology, 29(04), 4544 - 4555. Retrieved from