A machine learning algorithm based on circulating metabolic biomarkers offers improved predictions of neurological diseases

Abstract

Background and aimsA machine learning algorithm based on circulating metabolic biomarkers for the predictions of neurological diseases (NLDs) is lacking. To develop a machine learning algorithm to compare the performance of a metabolic biomarker-based model with that of a clinical model based on conventional risk factors for predicting three NLDs: dementia, Parkinson’s disease (PD), and Alzheimer’s disease (AD). Materials and methodsThe eXtreme Gradient Boosting (XGBoost) algorithm was used to construct a metabolic biomarker-based model (metabolic model), a clinical risk factor-based model (clinical model), and a combined model for the prediction of the three NLDs. Risk discrimination (c-statistic), net reclassification improvement (NRI) index, and integrated discrimination improvement (IDI) index values were determined for each model. ResultsThe results indicate that incorporation of metabolic biomarkers into the clinical model afforded a model with improved performance in the prediction of dementia, AD, and PD, as demonstrated by NRI values of 0.159 (0.039–0.279), 0.113 (0.005–0.176), and 0.201 (−0.021–0.423), respectively; and IDI values of 0.098 (0.073–0.122), 0.070 (0.049–0.090), and 0.085 (0.068–0.101), respectively. ConclusionThe performance of the model based on circulating NMR spectroscopy-detected metabolic biomarkers was better than that of the clinical model in the prediction of dementia, AD, and PD.