  The human gut microbiota is known to contribute to numerous physiological
functions of the body and also implicated in a myriad of pathological
conditions. Prolific research work in the past few decades have yielded
valuable information regarding the relative taxonomic distribution of gut
microbiota. Unfortunately, the microbiome data suffers from class imbalance and
high dimensionality issues that must be addressed. In this study, we have
implemented data engineering algorithms to address the above-mentioned issues
inherent 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 a
previously published dataset. The issue of class imbalance and high
dimensionality of the data was addressed through synthetic minority
oversampling technique (SMOTE) and principal component analysis (PCA). Our
results indicate that ensemble classifiers (RF and XGB decision trees) exhibit
superior classification accuracy in predicting the host phenotype. The
application of PCA significantly reduced testing time while maintaining high
classification accuracy. The highest classification accuracy was obtained at
the levels of species for most classifiers. The prototype employed in the study
addresses the issues inherent to microbiome datasets and could be highly
beneficial for providing personalized medicine.
