Application of Multiple Machine Learning Models based on Python in Predicting the Risk of Esophageal Cancer

Abstract

As health awareness rises and technology advances, machine learning has garnered significant attention in cancer prediction. This study focuses on esophageal cancer, a common and high-mortality digestive tract tumor, aiming to evaluate the predictive accuracy of different machine learning models, identify optimal models, and examine the role of hyperparameter tuning through random search in enhancing prediction accuracy for models with lower performance. The findings of the study indicate that Random Forest (RF), GradientBoosting(GB), and Extreme Gradient Boosting (XGBoost) perform best, with accuracy reach 1.00; the K-Nearest Neighbors (KNN) accuracy is 0.97; the Support Vector Classification (SVC) accuracy is about 0.58, and the SVC with random search for hyperparameter tuning reaches 0.97; the accuracy of logistic regression is 0.68, and after random search hyperparameter tuning, it can reach 0.83. This study provides meaningful insights into leveraging machine learning for cancer prediction, with the potential to enhance future diagnostic practices and therapeutic strategies.