A Novel Random Forest-SMOTE Framework With Polynomial Feature Engineering for Early Detection of Gastrointestinal Disorders

About 40% of people around the world have gastrointestinal (GI) problems. Gastrointestinal issues negatively impact the lives of millions of people and place significant stress on healthcare systems. The wrong diagnosis might make treatment less effective because symptoms like diarrhea, bloating, stomach pain, and liver problems often happen at the same duration. For early intervention and personalized care, it is essential to correctly and quickly classify these symptoms. We used the Random Forest model to create a strong classification framework for this study. It can tell if someone has one of seven main symptom groups: bloating, diarrhea or constipation, blood in the stool, unexplained weight loss, liver problems, nausea or vomiting, or abdominal cramps or pain. In our model we have used a multi-modal dataset that includes genetic markers, clinical assessments, lifestyle factors, and diagnostic imaging data to give a full picture of the diagnosis, and then we added polynomial feature engineering and other methods to the model to make it better at finding hidden and small patterns and relations in the parameters. We have also used the Synthetic Minority Oversampling Technique (SMOTE) to fix the problem of uneven data and GridSearchCV to make the model robust against hyperparameters. Although past studies have mostly focused on the lesion-based imaging or targeted disease identification, our model is a step forward in terms of symptom-based early classification with multi-modal tabular data. The model is capable of combining clinically informed two-factor interactions (e.g., stress × appetite loss, inflammation × bowel habits), which significantly improve the predictive performance and interpretability. This design reduces the disparity between the algorithmic predictions and the clinician thinking, thus, the system has become more applicable to realistic diagnostic decision-making. In this way, it allows for personalized treatment plans that make the disease curable and save time and risk of severity of GI disease. Future work will focus on adding biomarker data and real-time patient tracking to the framework so that it can be used easily in clinical decision-making.