Detection of cardiovascular disease using explainable artificial intelligence and gut microbiota data

Abstract

Purpose:

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. Artificial Intelligence (AI) may be a helpful tool to identify the hidden patterns in gut microbiota for the detection of disease and other classification problems.

Methods:

In this study, we propose a deep neural model based on a one-dimensional convolutional neural network (1D-CNN) to detect cardiovascular disease using bacterial taxonomy and OTU (Operational Taxonomic Unit) table data. The developed AI method is compared to classical machine learning algorithms, regression, boosting algorithms and a deep model, Tabular Network (TabNet), developed for tabular data and obtained outperforming classification results.

Results:

According to AUC (Area Under Curve) values, boosting and regression methods outperformed the classical machine learning methods. However, the highest value of 97.09 AUC was obtained with the developed 1D-CNN model by using bacterial taxonomy data even with less then expected number of samples. Using explainable AI, nine bacteria were identified which the models find important for classification.

Conclusion:

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. The study also investigated the effect on barcode sequence for the classification, but the result showed that barcode sequences do not contribute to the bacterial taxonomy data for the estimation of CVD disease.