AI Approach for Oncology using Mono-Cultured HGB Data from Various Methods

An understanding of the complex ecosystem in the human gut microbiome is essential in determining the cause of a variety of health conditions including cancer and other diseases. This research establishes an improved process of detecting and determining the bacterial species in the gut microbiome using advanced machine learning methods including feature engineering and a strong predictive model. This research used the Monoculture data set with predicted and observed microbial metabolic activities by using MMT., Micom and COMET as methods. The Random Forest is used to model and analyze the microbial data. Feature engineering., which selected different essential features such as predicted., expectation., and their differences., enabled perfect comparison of the same microbial species. When applying the Random Forest model., the most compatible with gut microbiome data., the result indicated that the model is meaningful and achieved a perfect test accuracy of 0.875. This high testing accuracy proved the same capability of the model to predict the presence of microbial species where the expected value and the difference between predicted and differences turn useful in distinguishing the variation in the microbial data. It is important to note that selecting appropriate and important features for use through the machine learning model facilitates the improved performance and accuracy of the microbiome. This research is important not just because it helps to understand how the microbes interact in the gut but also demonstrates the methodologies that can be applied to achieve a high level of accuracy in microbiome diagnosis.