Open AccessDementia Prediction on OASIS Dataset using Supervised and Ensemble Learning Techniques
Author(s) -
Shanmuga Skandh Vinayak E,
A. Shahina,
Nayeemulla Khan A
Publication year - 2020
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.a1827.1010120
Subject(s) - dementia , artificial intelligence , machine learning , computer science , boosting (machine learning) , gradient boosting , ensemble learning , supervised learning , pattern recognition (psychology) , artificial neural network , random forest , medicine , disease , pathology
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.