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

• Autorzy: Al-Kaltakchi Musab Tahseen Salahaldeen , Al-Nima Raid Rafi Omar , Alhialy Azza

Identyfikatory

DOI

10.24425/ijet.2024.149595

• Języki publikacji: EN

Abstrakty

EN

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 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.

Słowa kluczowe

EN

blood pressure; thyroid hormone; Artificial Intelligence; Fuzzy Logic; Adaptive Neuron-Fuzzy Inference System; ANFIS

• Wydawca: Polish Academy of Sciences, Committee of Electronics and Telecommunication
• Czasopismo: International Journal of Electronics and Telecommunications
• Rocznik: 2024
• Tom: Vol. 70, No. 3
• Strony: 667--672
• Opis fizyczny: Bibliogr. 29 poz., rys.

Twórcy

autor

Al-Kaltakchi Musab Tahseen Salahaldeen

• Mustansiriyah University, Department of Electrical Engineering, College of Engineering, Mustansiriyah University, Baghdad, Iraq

autor

Al-Nima Raid Rafi Omar

• Technical Engineering College of Mosul, Northern Technical University, Mosul, Iraq

autor

Alhialy Azza

• Technical Engineering College of Mosul, Northern Technical University, Mosul, Iraq

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).