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The human gut microbiota is known to contribute to numerous physiologicalfunctions of the body and also implicated in a myriad of pathologicalconditions. Prolific research work in the past few decades have yieldedvaluable information regarding the relative taxonomic distribution of gutmicrobiota. Unfortunately, the microbiome data suffers from class imbalance andhigh dimensionality issues that must be addressed. In this study, we haveimplemented data engineering algorithms to address the above-mentioned issuesinherent to microbiome data. Four standard machine learning classifiers(logistic regression (LR), support vector machines (SVM), random forests (RF),and extreme gradient boosting (XGB) decision trees) were implemented on apreviously published dataset. The issue of class imbalance and highdimensionality of the data was addressed through synthetic minorityoversampling technique (SMOTE) and principal component analysis (PCA). Ourresults indicate that ensemble classifiers (RF and XGB decision trees) exhibitsuperior classification accuracy in predicting the host phenotype. Theapplication of PCA significantly reduced testing time while maintaining highclassification accuracy. The highest classification accuracy was obtained atthe levels of species for most classifiers. The prototype employed in the studyaddresses the issues inherent to microbiome datasets and could be highlybeneficial for providing personalized medicine.

Application of data engineering approaches to address challenges in microbiome data for optimal medical decision-making