Ensembling Model Approach for Prediction of Pancreatic Cancer Using a Biomarker Panel and Multi-Model Classifiers

With its high mortality rate and devastating nature, pancreatic cancer requires early detection in order to improve treatment outcomes. This research investigates the dataset “Urinary biomarkers for pancreatic cancer” collected from urine and plasma samples, which includes important patient features such as patient cohort, age, sex, diagnosis, and a variety of biomarkers, all of which are crucial characteristics linked to the identification of pancreatic cancer. Employing thorough data preprocessing phase, that includes feature normalization, skewness assessment, outlier detection, including one-hot encoding for ‘age’, the research establishes a robust foundation for predictive modeling. The performance of three distinct classifiers―Random Forest, KNN, and XGBoost―is evaluated with emphasis on hyperparameter tuning. A significant addition is the Voting Classifier ensemble, strategically combining model strengths to achieve a remarkable 97% prediction accuracy. This research contributes to understanding patient-biomarker dynamics in pancreatic cancer, emphasizing machine learning's transformative role in advancing early detection for improved treatment outcomes.