Colorectal cancer (CRC) is the third most deadly cancer worldwide. The use of gut microbiome in early detection of the disease has attracted much attention from the research community, mainly because of its noninvasive nature. Recent achievements in next generation sequencing technology have led to increased availability of sequence data and enabled an environment for the growth of gut microbiome research. The use of conventional machine learning algorithms for automatic detection of CRC based on the microbiome is limited by factors such as low accuracy and the need for manual selection of features. Despite their success in other fields, Deep Neural Network (DNN) algorithms have limitations in microbiome-based CRC classification. These limitations include high dimensionality of microbiome data and other characteristics associated with sequence data such as feature dominance. In this paper, we propose a feature augmentation approach that aggregates data normalization methods to extend existing features of a dataset. The proposed method combines feature extension with data augmentation to improve CRC classification performance of a DNN model. The proposed model obtained area under the curve (AUC) scores of 0.96 and 0.89 on two publicly available microbiome datasets.

No abstract

No abstract

Machine learning (ML)-based detection of diseases using sequence based gut microbiome data has been of great interest within the artificial intelligence in medicine (AIM) community. The approach offers a non-invasive alternative in colorectal cancer detection, where data can be obtained from stool samples. Considering limitations of existing methods for CRC detection, medical research has shown interest in the use of high throughput data to identify the disease. Owing to several limitations of conventional ML algorithms, deep learning (DL) methods are becoming more popular due to their outstanding performance in related fields. However, the performance of DL methods is affected by limitations such as dimensionality, sparsity, and feature dominance inherent in microbiome data. This research proposes stacking and chaining of normalisation methods to address the limitations. While the stacking technique offers a robust, easy to use, and interpretable alternative for augmenting microbiome and other tabular data, the chaining technique is an alternative to data normalisation that dynamically adjusts the underlying properties of data towards the normal distribution. The proposed techniques are combined with rank transformation and feature selection to further improve the performance of the model, with area under the curve (AUC) values between 0.857 to 0.987using publicly available datasets.