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

Background and aims: A 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 methods: The 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. Results: The 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. Conclusion: The 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.