Vol. 3 No. 1 (2023): Journal of Millimeterwave Communication, Optimization and Modelling

Using ANFIS to Predict the Long and Short Term Stroke Risk Based on Ultrasound Carotid Imaging and Clinical data of Initially Asymptomatic Patients

Suhail Odeh
Bethlehem University
JOMCOM 3(1) Cover

Published 30.07.2023


  • Adaptive Network based Fuzzy Inference System (ANFIS),
  • stroke risk,
  • Feature Selection


The aim of this study is to investigate the development of predictive modelling in order to estimate the short (less or equal to three years) or long term (more than three years) stroke risk of patients with asymptomatic carotid artery stenosis. Data were collected from 108 patients that had a stroke event have been used. The prediction is done using base line data where patients were still asymptomatic. The information collected includes non-invasive ultrasound images of the carotid arteries and several other clinical data like patient’s blood tests (Cholesterol, creatinine, general blood parameters), diabetes, smoking, family history. Ultrasound images were analyzed and several features that can be used in order to characterize the type, size, structure and morphology of the atherosclerotic plaques where extracted. Based on the extracted image features and clinical data; we had created a risk modelling system based on Adaptive Network based Fuzzy Inference System (ANFIS). Model was investigated to classify the subjects into the two classes i) short (≤3 years) and ii) long term (>3 years) period stroke events. The ANFIS could give us correct classification rate up to 97±2.6%. These results can clearly indicate that ultrasound image plaque characteristics in combination with clinical data can be used in order to create predictive models for stroke risk period.


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