Detecting Anatomical Landmarks for Fast Alzheimer’s disease by Random Forest Classification

Authors

  • Ananthi N
  • S. Sakthi Prakash
  • J. Vigneshwaran
  • M. Kesav Ram

Abstract

Alzheimer’s disease is the most often dementia that is observed mostly in the elderly, wherein the brain memory related elements destroys results in cognitive imbalance and memory loss. In case of India, the Alzheimer’s disease is the most popular condition present around the elderly ages. The MRI or PET scan are the effective methodologies present to scan human brain and identify the Alzheimer’s spots present in the brain. But in case of Alzheimer’s disease, the early detection is quite a tedious process as the healthy people’s (HP) brain scan seems more or less similar to the Alzheimer’s disease patient’s scan (AP). So in-order to identify the Alzheimer’s disease in the early stages Random Forest Algorithm is used. The Random Forest Algorithm is a type of Ensemble learning algorithm that is employed for both classification and regression. The advantage of the algorithm is that Result’s accuracy obtained by the algorithm. The Random Forest algorithm identifies the minute variations associated with each and every data set provided for training and classifies them as separate classes based on their functionalities. Thus the prediction accuracy provided by the Random Forest Algorithm are pretty much efficient than the predecessors algorithms. The disadvantage with the regression is that the higher plan calculations provide less accurate results.

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Published

2020-04-09

Issue

Section

Articles