Estimating risk levels for blood pressure and thyroid hormone using artificial intelligence methods

Abstract

In this work, artificial intelligence methods are designed and adopted for evaluating various risk levels of thyroid hormone and blood pressure in humans. Fuzzy Logic (FL) method is firstly exploited to provide the risk levels. Additionally, a machine learning was proposed using the Adaptive Neuron- Fuzzy Inference System (ANFIS) to learn and assess the risk levels by fusing a multiple-layer Neural Network (NN) with the FL. The data are collected for standard risk levels from real medical centers. The results lead to well ANFIS design based on the FL, which can generate such interesting outcomes for predicting risk levels for thyroid hormone and blood pressure. Both proposed methods of the FL and ANFIS can be exploited for medical applications.