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A Cost-efficient Model for Predicting Cerebral Aβ Burden Using MRI and Neuropsychological Markers in the ADNI-2 Cohort: Comparisons in Predictability among Machine Learning Models
Version 1
: Received: 30 August 2020 / Approved: 31 August 2020 / Online: 31 August 2020 (09:30:08 CEST)
How to cite:
Ko, H.; Park, S.; Ihm, J. A Cost-efficient Model for Predicting Cerebral Aβ Burden Using MRI and Neuropsychological Markers in the ADNI-2 Cohort: Comparisons in Predictability among Machine Learning Models. Preprints2020, 2020080716 (doi: 10.20944/preprints202008.0716.v1).
Ko, H.; Park, S.; Ihm, J. A Cost-efficient Model for Predicting Cerebral Aβ Burden Using MRI and Neuropsychological Markers in the ADNI-2 Cohort: Comparisons in Predictability among Machine Learning Models. Preprints 2020, 2020080716 (doi: 10.20944/preprints202008.0716.v1).
Cite as:
Ko, H.; Park, S.; Ihm, J. A Cost-efficient Model for Predicting Cerebral Aβ Burden Using MRI and Neuropsychological Markers in the ADNI-2 Cohort: Comparisons in Predictability among Machine Learning Models. Preprints2020, 2020080716 (doi: 10.20944/preprints202008.0716.v1).
Ko, H.; Park, S.; Ihm, J. A Cost-efficient Model for Predicting Cerebral Aβ Burden Using MRI and Neuropsychological Markers in the ADNI-2 Cohort: Comparisons in Predictability among Machine Learning Models. Preprints 2020, 2020080716 (doi: 10.20944/preprints202008.0716.v1).
Abstract
Substantial studies have focused on early detection of Alzheimer's disease (AD). Cerebral amyloid beta (Aβ), is hallmark of AD, can be observed in vivo via positron emission tomography imaging using amyloid tracer or cerebrospinal fluid assessment, but costly expensive. The current study aims to identify and compared predictability in magnetic resonance imaging (MRI) markers and neuropsychological markers to predict cerebral Aβ status in AD cohort using machine learning (ML) approaches. The predictability in candidate markers for cerebral Aβ status was examined by analyzing 724 participants from the ADNI-2 cohort. Demographic variables, structural MRI markers, and neuropsychological test scores were used as input in several ML algorithms to predict cerebral Aβ positivity. Out of five combination of candidate markers, neuropsychological markers with demographics showed the most cost-efficient result. A feature selection model could distinguish abnormal levels of Aβ with the predictability of 0.85, indicating the same performance with MRI-based models. The result has first to identified the predictability in MRI markers using ML approaches, and secondary to demonstrate the neuropsychological model with demographics could predict Aβ positivity, suggesting a more cost-efficient method for detecting cerebral Aβ status compared to MRI markers.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Hyunwoong Ko