Machine Learning Models for Predicting Susceptibility to Infectious Diseases Based on Microbiome Profiles

Abstract

The human microbiome comprises complex ecosystems of microorganisms inhabiting different parts of the body and playing a very important role in sustaining healthiness and dictating disease vulnerability. Basing on this continuous generation of data on the microbiome, interest is developing in using them for disease risk prediction. Machine learning provides an extremely robust way of doing so, due to its nature in being able to handle complex and high-dimensional data. In this research article, authors have compared the efficiency of Random Forest, Support Vector Machines, and Neural Networks machine learning models in predicting infectious diseases using a microbiome profile. It provides a comprehensive overview of various studies that were published in the recent past using these machine learning techniques for microbiome data analysis. It further assesses the degree by which each model has captured the intrinsic complexities and variability of the microbiome that hold the key to the prediction of diseases with accuracy. Moreover, this review also underlined the importance of feature selection and data preprocessing in enhancing the performance of machine learning models. By selecting the most relevant features and properly preprocessing the data, one can train better models and hence make better predictions. Our results give a very good potential for machine learning models in predicting susceptibility to infectious diseases and, at the same time, show that there is indeed potential for further improvement. Multi-omics data integration should increase predictive power—incorporation of microbiome data with other kinds of biological information. Model interpretability can be important to enhancing clinicians' understanding and trust in the prediction, which is critical to the successful integration of these tools into truly personal healthcare.