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This study compares the performance of Decision Tree and Random Forest Classifier models on a binary classification medical data set that includes images of prostate cancer. The two models are evaluated through metrics like accuracy, precision, recall, F1 score, ROC curve, etc. After the testing, the random Forest classifier showed better performance than decision tree classifier. The random forest showed an accuracy of 85%, whereas the decision tree could only record an accuracy of 80%. The results mentioned that Random forest gives better performance than Decision tree.

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Comparative Analysis of Decision Tree and Random Forest Models for Prostate Cancer Classification

  • 会议论文

  • 发表日期：

    2026年

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  • Priya Mittal,
  • Bhisham Sharma,
  • D. P. Yadav,
  • Tapas Sharma,
  • Imed Ben Dhaou

摘要

This study compares the performance of Decision Tree and Random Forest Classifier models on a binary classification medical data set that includes images of prostate cancer. The two models are evaluated through metrics like accuracy, precision, recall, F1 score, ROC curve, etc. After the testing, the random Forest classifier showed better performance than decision tree classifier. The random forest showed an accuracy of 85%, whereas the decision tree could only record an accuracy of 80%. The results mentioned that Random forest gives better performance than Decision tree.