Machine Learning in Alzheimer’s Disease: Prognostic Prediction via Neuroimaging and Numerical Data

Authors

  • Ydathip Phetchrungruengphol The Newton Sixth Form School
  • Phasit Thanitkul The Newton Sixth Form School
  • Chavakorn Arunkunarax The Newton Sixth Form School
  • Thanapol Wongtharua The Newton Sixth Form School
  • Kunapas Sumpunwetchakul The Newton Sixth Form School
  • Jetnipat Kongsirirungruang The Newton Sixth Form School
  • Niracha Janavatara The Newton Sixth Form School
  • Witawin Sittisirinukul The Newton Sixth Form School
  • Chutikarn Kanchana-art The Newton Sixth Form School
  • Pingpan Krutdumrongchai The Newton Sixth Form School
  • Tansiri Praditphonlert The Newton Sixth Form School
  • Kanokkorn Pitayakornpakdee The Newton Sixth Form School

DOI:

https://doi.org/10.47611/jsrhs.v11i3.2878

Keywords:

brain imaging, support vector machine (SVM), presymptomatic diagnosis, genetic risks, Alzheimer's Disease

Abstract

Alzheimer's disease (AD) is often detected too late or inaccurately in clinical practice. Therefore, improvement in the current methods of AD detection will provide opportunities for early intervention, symptomatic treatment, and, overall, better quality of patients’ and their caregivers’ lives. The paper is an in-depth study of how functional brain imaging and support vector machine (SVM) could be utilized to detect the risk for AD which works by assessing and plotting data into multi-dimensional graphs for various results. It aims to identify patients in presymptomatic stages for early treatment to delay or prevent progressive cognitive decline and disease. With knowledge of machine learning, our medical tool is a breakthrough in the methodology of AD detection. The future of our tool requires a substantive amount of brain scan data for the machine learning algorithm to produce reliable results, so further research in this field of study is crucial and strongly encouraged. 

 

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Published

08-31-2022

How to Cite

Phetchrungruengphol, Y., Thanitkul, P., Arunkunarax, C., Wongtharua, T., Sumpunwetchakul, K., Kongsirirungruang, J., Janavatara, N. ., Sittisirinukul, W., Kanchana-art, C., Krutdumrongchai, P., Praditphonlert, T., & Pitayakornpakdee, K. . (2022). Machine Learning in Alzheimer’s Disease: Prognostic Prediction via Neuroimaging and Numerical Data. Journal of Student Research, 11(3). https://doi.org/10.47611/jsrhs.v11i3.2878

Issue

Vol. 11 No. 3 (2022)

Section

HS Review Articles

Copyright (c) 2022 Ydathip Phetchrungruengphol, Phasit Thanitkul, Chavakorn Arunkunarax, Thanapol Wongtharua, Kunapas Sumpunwetchakul, Jetnipat Kongsirirungruang, Niracha Janavatara, Witawin Sittisirinukul, Chutikarn Kanchana-art, Pingpan Krutdumrongchai, Tansiri Praditphonlert; Kanokkorn Pitayakornpakdee

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