Simultaneous detection of multiple inherited metabolic diseases using GC-MS urinary metabolomics by chemometrics multi-class classification strategies.

Metabonomics has been widely used in disease diagnosis and clinically practical methods often require the detection of multi-class bio-samples. In this work, multi-class classification methods were investigated to simultaneously discriminate among 6 inherited metabolic diseases (IMDs) and the normal instances using gas chromatography-mass spectrometry (GC-MS) of urine samples. Two common multi-class classification strategies, one-against-all (OAA) and one-against-one (OAO) were compared and enhanced using a novel ensemble classification strategy (ECS), which developed a set of sequential sub-classifiers by fusion of OAA and OAO and made the final classification decisions using softmax function. GC-MS data of 240 instances of 6 IMDs and healthy controls were classified by different strategies based on orthogonal partial least squares discriminant analysis (OPLS-DA) and particle swarm optimization (PSO) algorithm was performed for feature selection. By OAA and OAO, the classification accuracies were 70.00% and 82.86%, respectively. Using the two methods based on ECS, the total classification accuracies were 0.9143 and 0.9429. The newly proposed ECS will provide a useful multi-class classification tool for simultaneous detection of clinically similar IMDs and promote practical and reliable diagnosis of IMDs using metabonomics data.