Title: Gradient boosting tree for 1H-MRS Alzheimer diagnosis

Authors: Defu Liu; Guowu Yang; Yuchen Li; Jinzhao Wu; Fengmao Lv

Addresses: School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China ' School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China ' West China Centre of Medical Sciences, Sichuan University, Chengdu, Sichuan, China ' School of Computer, Electronics and Information, Guangxi University, Nanning, Guangxi, China ' Centre of Statistical Research and School of Statistics, Southwestern University of Finance and Economics, Chengdu, Sichuan, China; Network and Data Security Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, Sichuan, China

Abstract: In recent years, increasing attention is drawn to Early-Onset Alzheimer's Disease (EOAD). As effective biomarkers for EOAD, the brain metabolites, measured by proton magnetic resonance spectroscopy (1H-MRS), are significantly sensitive to the brain metabolite changes in dementia patients. This work aims to design an effective EOAD computer-aided system through mining the 1H-MRS data with advanced machine learning techniques. Specifically, our method first adopts Gradient Boosting Decision Tree (GBDT) to learn the 1H-MRS biomarkers of EOAD patients, which are then used to construct the final classifier for Alzheimer diagnosis. To validate our proposal, we have conducted comprehensive experiments for evaluation and the experimental results clearly demonstrate the effectiveness of our method.

Keywords: early-onset Alzheimer's disease; proton magnetic resonance spectroscopy; Alzheimer biomarker; gradient boosting decision tree.

DOI: 10.1504/IJDMB.2020.10027151

International Journal of Data Mining and Bioinformatics, 2020 Vol.23 No.1, pp.12 - 29

Received: 02 Aug 2019
Accepted: 02 Aug 2019

Published online: 28 Feb 2020 *

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