_applied_  
_sciences_  
_Article_  
Understanding the Role of the Microbiome in Cancer  
Diagnostics and Therapeutics by Creating and Utilizing  
ML Models  
**[PERSON] 1,*(r)**, **[PERSON] 2,*(r)**, **[PERSON] 1(r)**, [PERSON] 3  
and [PERSON] 2  
1 Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University,  
1000 Skopje, North Macedonia; [EMAIL_ADDRESS]  
2 Biostatistics and Medical Informatics Department, Acibadem Mehmet Ali Aydinlar University,  
stanbul 34752, Turkey; info(ducbachdem.edu  
3 Epigenetics Genetics Bioinformatics Software Inc., Istanbul 34083, Turkey;  
[EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS] (M.C. ); [PERSON]_ADDRESS] (M.J.O.) **Simple Summary:** Cancer is one of the leading causes of death worldwide. Colorectal cancer belongs to the group of the most malignant tumors for which their burden can be only reduced through early detection and appropriate treatment. Increasing evidence indicates that the intestine microbiota is related and can impact colorectal carcinogenesis. This study proposes a multidisciplinary approach of two-phase methodology for modeling and interpreting the key biomarkers that can play a significant role in understanding the drug-resistant mechanism for patients diagnosed with colorectal cancer. The proposed methodology was evaluated using a publicly accessible dataset, which may serve clinicians as a complementary analysis tool in colorectal cancer diagnostics and therapeutics. This study contributes to the field of predictive modeling in healthcare. Recent studies have highlighted that gut microbiota can alter colorectal cancer susceptibility and progression due to its impact on colorectal carcinogenesis. This work represents a comprehensive technical approach in modeling and interpreting the drug-resistance mechanisms from clinical data for patients diagnosed with colorectal cancer. To accomplish our aim, we developed a methodology based on evaluating high-performance machine learning models where a Python-based random forest classifier provides the best performance metrics, with an overall accuracy of 91.7%. Our approach identified and interpreted the most significant genera in the cases of resistant groups. Thus far, many studies point out the importance of present genera in the microbiome and intend to treat it separately. The symbiotic bacterial analysis generated different sets of joint feature combinations, providing a combined overview of the model's predictiveness and uncovering additional data correlations where different genera joint impacts support the therapy-resistant effect. This study points out the different perspectives of treatment since our aggregate analysis gives precise results for the genera that are often found together in a resistant group of patients, meaning that resistance is not due to the presence of one pathogenic genus in the patient microbiome, but rather several bacterial genera that live in symbiosis. colorectal carcinogenesis; feature subset selection; machine learning; postsurgical risk; random forest; colorectal cancer; gut microbiota; therapy resistance; microbiome; methodology  
**1. Introduction**  
It is estimated that there will be 19.3 million new cancer cases, of which 10% will be colorectal cancer (CRC), considering the statistics from 2020. Furthermore, out of 10 million cancer deaths, 9.4% are due to colorectal cancer [1]. Accordingly, this emerging evidence suggests that CRC is one of the most common malignant tumors, ranking in the top three causes of cancer-related death. The high mortality rate of CRC patients may be due to many genetic and environmental factors. One of the causes for the high mortality rate is the unreliable treatment of patients with colorectal cancer due to the gut microbiota [2]. The human intestine contains approximately 7000 different strains of bacteria in the intestinal region that represents an approximate weight of nearly two kilos [3].