Detection of Cardiovascular Disease Using Gut Microbiota Data

Abstract

Abstract Gut microbiota are defined as the microbial population of the intestines. They include various types of bacteria which can influence and predict the existence or onset of some specific diseases. Therefore, it is a common practice in medicine to analyze the gut microbiota for diagnostic purposes by analyzing certain measurable biochemical features associated with the disease under investigation. However, the evaluation of all the data collected from the gut microbiota is a labor-intensive process. Machine learning algorithms may be a helpful tool to identify the hidden patterns in gut microbiota for the detection of disease and other classification problems. In this study, we propose a deep neural model based on 1D-CNN to detect cardiovascular disease using bacterial taxonomy and OTU (Operational Taxonomic Unit) table data. The developed method is compared to classical machine learning algorithms, regression, boosting algorithm and a deep model, TabNet, developed for tabular data and obtained outperforming classification results. The proposed method is robust and well adapted to taxonomy data in tabular form. It can be easily adapted to detect other diseases by using taxonomy data.