Hybridization Of Neuro Fuzzy With Particle Swarm Algorithm For Health Iot - Devi P,Sathyalakshmi S,Venkata Subramanian D (International Journal of Advanced Science and Technology, 2020)
Remote Monitoring in IoT plays a major  role in Smart Health Data. Sensor data collected and sent for pre-diagnosis of disease with respect to blood pressure readings. Prediction of  the severity of  disease is detected since hypertension is a sickness which  results in  life threatening diseases like renal failure, heart attack, stroke and leads to major complications if undiscovered. This model is been developed to preprocess the data to diagnose and treat the hypertension patients leads to an approach of developing  a hybridization of neuro fuzzy with particle swarm optimization (HNFPSO) algorithm to diagnose  based on a set of symptoms and rules. The HNFPSO algorithm is presented as an expert system for the pre-processing and  classification of blood pressure (BP). The HNFPSO algorithm makes use of neural network (NN), fuzzy logic and PSO algorithm. The presented HNFPSO algorithm intends to pre-process  the characteristics of hypertension for classification by the use of rules provided by an expert in fuzzy system based on rules provided by an expert. Here, the rules takes place using PSO algorithm for getting optimal number of rules with maximum classification results. The proposed model is validated using a benchmark hypertension dataset and the simulation results indicated the betterment of the HNFPSO algorithm in IoT framework.