Iskandar, Muhammad Yashlan and Nugroho, Handoyo Widi (2025) Comparative Evaluation of Decision Tree and Random Forest for Lung Cancer Prediction Based on Computational Efficiency and Predictive Accuracy. Comparative Evaluation of Decision Tree and Random Forest for Lung Cancer Prediction Based on Computational Efficiency and Predictive Accuracy, 6 (5). pp. 3392-3404. ISSN 2723-3871
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Abstract
Early detection of lung cancer is essential for improving treatment outcomes and patient survival rates. This paper presents a comparative evaluation of two classification algorithms: Decision Tree and Random Forest, focusing on both predictive performance and computational efficiency. The models were tested using 10-fold cross-validation to ensure robustness. Both algorithms achieved the same accuracy of 93.3%. However, Random Forest slightly outperformed Decision Tree in recall (88.8% vs. 87.9%), F1-score (92.2% vs. 92.1%), and AUC (0.94 vs. 0.91), while Decision Tree obtained higher precision (97% vs. 95.9%). In terms of computational efficiency, Decision Tree demonstrated faster training and testing times, lower memory usage, and reduced energy consumption compared to Random Forest. The results reveal a clear trade-off between prediction quality and resource usage, highlighting the importance of selecting algorithms not only for their accuracy but also for their practicality in real-world healthcare scenarios. This comprehensive evaluation provides valuable insights for developing intelligent decision support systems that are both effective and resource-efficient, especially in environments with limited computing capacity. These findings contribute to the advancement of resource-aware intelligent systems in the field of medical informatics.
| Item Type: | Article |
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| Subjects: | Ilmu Komputer eTheses |
| Divisions: | Pasca Sarjana > Magister Teknik Informatika |
| Depositing User: | Muhammad Yashlan Iskandar |
| Date Deposited: | 27 Oct 2025 02:15 |
| Last Modified: | 27 Oct 2025 02:15 |
| URI: | http://repo.darmajaya.ac.id/id/eprint/23007 |
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