Machine Learning-Assisted Analysis Of Pain Reduction: Evaluating Pre-Operative Oral Metronidazole In Patients Undergoing Open Hemorrhoidectomy

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Background: The management of postoperative pain in hemorrhoidectomy patients represents a major clinical challenge because preoperative oral metronidazole shows inconsistent treatment results. Subjective pain assessment tools used in traditional methods result in inconsistent treatment results because they depend on patient-reported measurements. Machine learning models facilitate data-based assessment of pain within clinical settings which enables better pain management decisions thus helping patients to recover effectively. Objective: The research will evaluate the effectiveness of oral metronidazole given before surgery for pain reduction while building a predictive model with machine learning to determine significant pain score predictors at the 24-hour postoperative period. Methods: The researchers performed a retrospective observational study that analyzed clinical data from hemorrhoidectomy patients. The data collection included patient age and BMI along with surgical details about operative time and removed hemorrhoids and pain scores measured at 2h, 6h, 12h, 24h along with medication usage records. The analysis included exploratory data examination and feature importance evaluation to determine significant factors affecting postoperative pain. An evaluation using Gradient Boosting together with Random Forest and Linear Regression and XGBoost occurred within an 80:20 train-test split configuration to assess model performance. The model evaluation used Mean Absolute Error (MAE) alongside Mean Squared Error (MSE) and R² score for assessment. Results: The Random Forest model proved to be the most effective algorithm due to its R² score of 0.78 and its minimal MAE (0.63) and MSE (0.87), indicating excellent predictive reliability. The analysis of feature importance showed medication administration (MED) stood as the main predictor variable and BMI and early postoperative pain levels at 12 hours ranked as secondary predictors. Early pain control interventions become essential due to the strong relationship found between pain scores obtained at 12 hours and 24 hours. Research findings indicate that patients with elevated BMI values together with increased 12-hour postoperative pain scores develop prolonged pain symptoms thus requiring individualized pain management approaches. Conclusion: Both the effectiveness of preoperative oral metronidazole in pain reduction together with the superior performance of machine learning models to predict pain trajectory outcomes are validated through this research. AI-driven predictive models launched into clinical decision systems create precise pain management through real-time identification of risks and customized analgesic treatment approaches. Additional research should employ multiple health care facilities with real-time patient tracking systems to confirm and enhance pain management methods.

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