Abstract
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.
Highlights
Dementia – A severe disorder that impacts the memory, thinking and communication capability of the brain,that affects over 50 million individuals world-wide according to Statista [1]
The Magnetic Resonance Imaging (MRI) scans data along with demographic assessments data such as the Mini-Mental State Exam (MMSE) scores, the education level of the subject, the socio-economic status of the subject, etc. collectively named as MR(Magnetic Resonance) session, are considered in developing machine learning models that predicts the presence of dementia in the subject
The experiment models are trained and tested using the data obtained from the MRI scans and the demographic data obtained during each scan from the test subjects(MR session)
Summary
Dementia – A severe disorder that impacts the memory, thinking and communication capability of the brain,that affects over 50 million individuals world-wide according to Statista [1]. A fast and simple system capable of identifying the presence of dementia, utilizing the clinical and demographic data of a person could be effective in providing swift diagnosis. In this experiment, the Magnetic Resonance Imaging (MRI) scans data along with demographic assessments data such as the Mini-Mental State Exam (MMSE) scores, the education level of the subject, the socio-economic status of the subject, etc. Collectively named as MR(Magnetic Resonance) session, are considered in developing machine learning models that predicts the presence of dementia in the subject.
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