Alzheimers disease is one of the most fastest growing and costly diseases in the world today. It affects the livelihood of not just patients, but those who take care of them, including care givers, nurses, and close family members. Current progression monitoring techniques are based on scans from MRI and PET, which can be inconvenient for patients to use. In addition, more intelligent and efficient methods are needed in order to provide predictions on what the current stage of the disease is and strategies on how to slow down its progress over time. Machine learning has been around for several decades and has recently been making important contributions in medical applications. While machine learning methods have been utilised for diagnosing Alzheimers disease, they focus on using image data from MRI and PET scans, which can be difficult for patients to obtain. In this paper, machine learning was used with RF data captured from 9 different head models showing different stages of Alzheimers disease. The RF data was processed in several machine learning algorithms. Each machine learning models prediction and accuracy was generated and the results were compared to determine which machine algorithm could be used to classify different stages of Alzheimers disease using RF data that was obtained noninvasively. Results from the study showed that overall the logistic regression model had the best accuracy of 98.97% and efficiency in differentiating between 4 different stages of Alzheimers dise
Original languageEnglish
Number of pages1
JournalIEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology
Early online date9 Jul 2021
Publication statusE-pub ahead of print - 9 Jul 2021


  • Diseases
  • Brain modeling
  • Alzheimer's disease
  • Radio frequency
  • Support vector machines
  • RF signals
  • Machine learning algorithms


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