Estimating Risk Levels for Blood Pressure and Thyroid Hormone Using Artificial Intelligence Methods
Abstract
In this work, artificial intelligence methods aredesigned 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 NeuronFuzzy 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.
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