Soft-Voting Ensemble Model: An Efficient Learning Approach for Predictive Prostate Cancer Risk

Abstract

Prostate cancer is increasingly common among men. However, the process of diagnosing malignant disease is relatively complicated and time-consuming. Identifying benign or malignant tumors early can assist medical professionals in choosing appropriate treatment methods. Consequently, we introduce a soft-voting ensemble model comprising several single machine learning models such as Logistic Regression, Random Forest, XGBoost, LGBM, and Support Vector Machine for the classification task with the prostate cancer dataset. The dataset was divided into two parts for training and testing with a ratio of 67:33. The confusion matrix was used to evaluate the performance of both the individual and ensemble models. Experimental results show that ensemble models achieve performance ranging from 87.88% to 96.97%, which is 3% to 9% better than individual models, surpassing recent research. Integrating the strengths of individual models helps minimize errors, resulting in optimal classification with high accuracy and overall performance in the field of machine learning.

Keywords

• Prostate cancer
• prediction model
• machine learning
• ensemble models
• random forest
• logistic regression