Predicting Gut Health Using MI Techniques

Digestion, immunity, and mental health are all impacted by gut health, which is crucial to general wellbeing. However, because the gut microbiota is dynamic and diverse, evaluating and forecasting gut health can be challenging. Through the analysis of microbiome data, food patterns, and lifestyle characteristics, this study investigates the potential of machine learning (ML) techniques to predict gut health. Overall health is greatly influenced by gut health, and imbalances in the gut microbiota have been connected to a number of illnesses. Predicting gut health based on food patterns, clinical indicators, and microbiome composition is made possible by machine learning (ML) techniques. This study investigates the classification and prediction of gut health status using the Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting (GB) algorithms. To improve model accuracy, feature selection, data preparation methods, and hyperparameter tuning were used. Gradient Boosting surpassed RF and SVM in terms of predictive capability, according to performance evaluation utilizing measures including accuracy, precision, recall, and F1-score. According to the results, ML-driven methods can evaluate gut health in an efficient manner, offering insightful information for early illness detection and individualized treatment. Keywords: Gut health, microbiome, machine learning, gut microbiota, health analytics, feature engineering, personalized nutrition, data-driven healthcare.