Neural Cryptography with Fog Computing Network for Health Monitoring Using IoMT

Sleep apnea syndrome (SAS) is a breathing disorder while person asleep. The traditional method for examining SAS Polysomnography (PSG). standard procedure of PSG requires complete overnight observation in laboratory. typically provides accurate results, but it expensive and time consuming. However, people with (SA), available beds laboratories are limited. Resultantly, may produce inaccurate diagnosis. Thus, this paper proposes the Internet Medical Things (IoMT) framework machine learning concept fully connected neural network (FCNN) k-nearest neighbor (k-NN) classifier. This describes smart monitoring patient’s sleeping habit diagnosis SA using FCNN-KNN+ average square error (ASE). For diagnosing SA, Oxygen saturation (SpO2) sensor device popularly used heart rate blood oxygen level. information securely stored IoMT fog computing network. Doctors can carefully monitor patient remotely on basis values, which efficiently proposed technique takes less than 0.2 s an accuracy 95%, higher existing models.