Impact of machine learning algorithms on disease prediction using microbiome data

Abstract

Microbiome acts pervasive roles in different environments. Microbiomes have a huge impact on human health and wealth. The human microbiome resides on and inside the human body. It carries out beneficial advantages in the human body such as metabolism, digestion of foods and regulates immune systems, etc. Likewise, an imbalance of the measure count of the microbiome causes symptoms of diseases. Earlier prediction and identification of diseases and taking precautions may help to reduce the loss of living beings and prevent the high risk of contagious diseases. The study of microbiome genetics leads to help in disease prediction. Microbiome data provides different features to predict diseases. Machine learning algorithms are used to predict diseases based on microbiome data. Machine learning algorithms are user-friendly methods to predict diseases. They provide fast outputs at a cheaper cost and help to predict future opportunities as well. To get the best algorithm for the prediction, we need to extensively search and experimentally select it. This work evaluates the impact of a set of machine learning algorithms for the prediction of disease using microbiome data.