Can Tactile Internet be a Solution for Low Latency Heart Disorientation Measure: An Analysis

To reduce the delay for accessing real-time data access from various applications (healthcare, transportations, virtual reality etc.), there is an exponential increase in the usage of Tactile Internet (TI) technology in recent era. Motivated from this, in this paper, we propose a TI-based random forest (RF) learning algorithm for heart disease predictions. The aim of this paper is to monitor and analyse the human activities for real-time data collection. The proposed approach is an analysis of heart ailments and can be used regularly for the health measure. For this purpose, the RF model is trained to map the collected sensor data features to output normal and abnormal states of the patient suffering from heart disorientation. Moreover, it removes excessive dependence on input values and cover possible alternate paths. Simulated results demonstrate that the proposed approach reduces the average delay and provides less training time in comparison to the pre existing conventional techniques.

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