Machine Learning Based Clinical Trial Performance Prediction for Medical Industry Applications
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V7I3P101Keywords:
Machine Learning, Clinical Decision Support, Risk Stratification, Deep Learning, Electronic Health Records, Artificial Intelligence, Predictive ModelingAbstract
Clinical trials are essential for evaluating the safety and effectiveness of medical treatments; however, they often involve high costs, long durations, and uncertain outcomes. This study investigates the possibility of predicting clinical trial results using machine learning (ML) techniques by analyzing patients’ intraoperative and postoperative adverse reactions, postoperative vital signs, and satisfaction levels in trials of new sedative drugs. A Clinical Trials dataset comprising 13,748 records and 11 attributes was utilized, followed by comprehensive preprocessing steps including missing value handling, label encoding, feature scaling, and data balancing using SMOTE to address class imbalance. Machine learning models including Logistic Regression (LR), Random Forest (RF), Convolutional Neural Networks (CNN), and the proposed Extreme Gradient Boosting (XGBoost) were implemented for comparative analysis. Experimental results demonstrate that XGBoost outperforms all other models with an accuracy of 92.7%, precision of 95.6%, recall of 95.9%, and F1-score of 95.7%, indicating superior predictive capability and robustness. The comparative analysis confirms that ensemble-based learning methods, particularly XGBoost, are highly effective for clinical trial outcome prediction, offering improved reliability and decision-support potential in medical research.
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