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Published October 30, 2020 | Version v1
Journal article Open

Dementia Prediction on OASIS Dataset using Supervised and Ensemble Learning Techniques

  • 1. Department of Information Technology, Sri Sivasubramaniya Nadar College of Engineering, Chennai, India.
  • 2. A, School of Computing Sciences and Engineering, Vellore Institute of Technology, Chennai, India.
  • 1. Publisher

Description

The Magnetic Resonance Imaging (MRI) data, which are a prevalent source of insight in understanding the inner functioning of the human body is one of the most preliminarymechanisms in the analysis of the human brain, including and not limited to detecting the presence of dementia. In this article, 7 machine learning models are proposed in the analysis and detection of dementiain the subjects ofOpen Access Series of Imaging Studies(OASIS) Brains 1, using OASIS 2 MRI and demographic data. The article also compares the performances of the machine learning models in terms of accuracy and prediction duration. The proposed model, eXtreme Gradient Boosting (XGB) algorithm performs with the highest accuracy of 97.87% and the fastest prediction durationof 0.031s/sample.

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Is cited by
Journal article: 2249-8958 (ISSN)

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ISSN
2249-8958
Retrieval Number
100.1/ijeat.A18271010120