Prevention for Automated Predict Outbreak of Epidemic Diseases Using Iot Based Global Pre-Emptive Scheme

Abstract

Preventing epidemics is the most important way to fight for the global spread of disease. Therefore, develop a safety and Health Monitoring wearable IoT enables real-time health monitoring to improve global health. However, it remains a very difficult method to establish preventive methods because epidemics are nonlinear and dynamic systems. The data integration of IoT is a big concern to predict the disease. So to deal this scenario, a novel Classification and regression model (CART) has been proposed to predict label for the data points. The corresponding labels are designated by traversing a binary tree according to the kernel,it overcomes scaling, outliers, and missing data information. When the data are successfully integrated but there is the problem with noise and demographic bias with the prediction of the disease. Hence a novel Imbedded Least Square Support Regression (ILS-SVR)which reduces random noise and removes drifts. In addition to mitigate the demographic distortion of an unexpected regression problem, the theory by-law function (TBF) has been implemented. Furthermore,helps to better predict the epidemic disease and for prevention strategy among the people, human mobility plays a major concern. In order to tackle the human mobility, a novel IoT based global Preemptive scheme has been introduced to track the pattern and the harmful symptoms of infectious patients are observed. This explores the possible role of K-NN machine learning techniques as a Hidden Markov Model (HMM). Thus automated alert systems prevent the mortality, morbidity rate, and timely detection of epidemic diseases with high efficiency.