A Deep Learning Approach to Predict Health Status Using Microbiome Profiling

The human microbiome consists of the microbes that live in and on our body and it plays an important part in our life helping in digestion, developing immunity, etc. There are mounting evidence that shows the relationship between the composition of the gut microbiome and the occurence of various diseases such as type 2 diabetes, colorectal cancer and liver cirrhosis. Machine learning approaches have been used to predict the likelihood of having such diseases using the microbial profile of an individual. In this paper, we explore a deep learning approach to predict health status of an individual utilizing their gut microbial profile. Health state prediction was performed using a dataset containing healthy and non-healthy raw shotgun stool metagenomics samples profiled at species and genera levels. The species level profiles outperformed the genera level profiles in terms of AUC giving a score of 0.957. The best performance for the deep learning approach recorded an AUC score of 0.98 for species level profiles when the sparseness in the datasets were removed. Further, the deep learning approach was able to outperform the state of the art approach.