Boosting 1H-MRS Alzheimer Diagnosis with Boosted Trees

Nowdays, more and more attention has been attached to early-onset alzheimer's disease (EOAD). It is well known that the Presenilin-1 gene (PSEN1) penetrant mutation can cause EOAD, and subjects with the PSEN1 p.Glu280Ala (E280A) mutation are at high risk of EOAD. Sensitive to the the progress of dementia, the brain metabolites measured by proton magnetic resonance spectroscopy (¹H-MRS) can be used as informative biomarkers of EOAD. In this work, we focus on designing an effective diagnosis framework for EOAD caused by E280A mutation through the combination of ¹H-MRS biomarkers and machine learning techniques. Specifically, we utilize gradient boosting decision tree (GBDT), which is an advanced machine learning tool, to analyze the physiological characteristics of EOAD patients and E280A mutation carriers. According to the relative importance of each ¹H-MRS variable provided by GBDT, informative brain metabolites are selected out as biomarkers of AD symptoms. Combining the ¹H-MRS biomarkers with GBDT, our method can achieve desirable predictive performance.¹MCI is the transitional state of AD, which involves early cognitive impairments [4].