Comparative Evaluation of Decision Tree and Random Forest for Lung Cancer Prediction Based on Computational Efficiency and Predictive Accuracy

Muhammad Yashlan Iskandar; Handoyo Widi  Nugroho

doi:10.52436/1.jutif.2025.6.5.4877

Authors

Muhammad Yashlan Iskandar Master’s Program Informatics Engineering, IIB Darmajaya, Indonesia
Handoyo Widi Nugroho Master’s Program Informatics Engineering, IIB Darmajaya, Indonesia

DOI:

https://doi.org/10.52436/1.jutif.2025.6.5.4877

Keywords:

Classification Performance, Computational Efficiency, Decision Tree, Lung Cancer, Random Forest, Supervised Learning

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.

Downloads

Download data is not yet available.

References

International Agency for Research on Cancer (IARC), “Lung Cancer.” Accessed: Feb. 06, 2025. [Online]. Available: https://www.iarc.who.int/cancer-type/lung-cancer/

E. Safitri, D. Rofianto, N. Purwati, H. Kurniawan, and S. Karnila, “Prediksi Penyakit Diabetes Melitus Menggunakan Algoritma Machine Learning,” Jurnal Sistem dan Teknologi Informasi (JUSTIN), vol. 12, no. 4, pp. 760–766, Oct. 2024, doi: 10.26418/justin.v12i4.84620.

A. C. Pacurari et al., “Diagnostic Accuracy of Machine Learning AI Architectures in Detection and Classification of Lung Cancer: A Systematic Review,” Diagnostics, vol. 13, no. 13, pp. 1–12, Jul. 2023, doi: 10.3390/diagnostics13132145.

Y. Wiratama and R. Abdul Aziz, “Perbandingan Prediksi Penyakit Stunting Balita Menggunakan Algoritma Support Vektor Machine dan Random Forest,” Technology and Science (BITS), vol. 6, no. 2, pp. 1159–1168, Sep. 2024, doi: 10.47065/bits.v6i2.5543.

Khodijah, Sriyanto, R. Abdul Aziz, and Suhendro, “Perbandingan Kinerja Lima Algoritma Klasifikasi Dasar Untuk Prediksi Penyakit Jantung ‘Classifier: NB, DTC4.5, KNN, ANN & SVM,’” Jurnal Jaringan Sistem Informasi Robotik (JSR), vol. 8, no. 2, pp. 230–234, Sep. 2024, doi: 10.58486/jsr.v8i2.

I. Akbar, F. Supriadi, and D. I. Junaedi, “Pemanfaatan Machine Learning di Bidang Kesehatan,” JATI (Jurnal Mahasiswa Teknik Informatika), vol. 9, no. 1, pp. 1744–1749, Feb. 2025, doi: 10.36040/jati.v9i1.12663.

M. Andani, J. Triloka, S. Y. Irianto, and H. W. Nugroho, “Performance Comparison of K-Nearest Neighbor, Naive Bayes, and Random Forest Algorithms in Obesity Prediction,” Sinkron: Jurnal dan Penelitian Teknik Informatika, vol. 9, no. 1, Jan. 2025, doi: 10.33395/sinkron.v9i1.14478.

M. S. Hasibuan and D. Fransisca, “Prediksi Stroke Otak Menggunakan Algoritma Naïve Bayes dan Particle Swarm Optimization (PSO),” INTEGER: Journal of Information Technology, vol. 9, no. 1, pp. 109–118, Mar. 2024, doi: 10.31284/j.integer.0.v9i1.5738.

M. Haris Luthfi and Chairani, “Penerapan Algoritma C4.5 Berbasis Particle Swarm Optimization (PSO) Untuk Deteksi Kanker Payudara,” JUPITER: Jurnal Penelitian Ilmu Dan Teknologi Komputer, vol. 16, no. 2, pp. 613–622, Oct. 2024, doi: 10.5281/zenodo.13293601.

J. Fahmi Idris, R. Ramadhani, and M. Malik Mutoffar, “Klasifikasi Penyakit Kanker Paru Menggunakan Perbandingan Algoritma Machine Learning,” JURNAL MEDIA AKADEMIK (JMA), vol. 2, no. 2, pp. 1981–2000, Feb. 2024, doi: 10.62281/v2i2.145.

M. F. A. Sayeedi, J. F. Deepti, A. M. I. M. Osmani, T. Rahman, S. S. Islam, and M. M. Islam, “A Comparative Analysis for Optimizing Machine Learning Model Deployment in IoT Devices,” Applied Sciences, vol. 14, no. 13, pp. 1–18, Jul. 2024, doi: 10.3390/app14135459.

N. V. D. S. S. V. Prasad Raju and P. N. Devi, “A Comparative Analysis of Machine Learning Algorithms for Big Data Applications in Predictive Analytics,” International Journal of Scientific Research and Management (IJSRM), vol. 12, no. 10, pp. 1608–1630, Oct. 2024, doi: 10.18535/ijsrm/v12i10.ec09.

Q. Lan, R. Wang, and H. Fan, “Early Diagnosis and Classification of Lung Cancer Driven by Multi-Feature Data: A Comparison and Optimization of Three Machine Learning Methods,” J Mech Med Biol, vol. 24, no. 9, pp. 1–21, Nov. 2024, doi: 10.1142/S0219519424400797.

H. Oktavianto, H. W. Sulistyo, G. Wijaya, D. Irawan, and G. Abdurrahman, “Analisis Komparasi Kinerja Metode Decision Tree dan Random Forest dalam Klasifikasi Teks Data Kesehatan,” Bina Insani ICT Journal, vol. 11, no. 1, pp. 56–65, Jun. 2024, doi: 10.51211/biict.v11i1.2928.

M. B. A. Darmawan, F. Dewanta, and S. Astuti, “Analisis Perbandingan Algoritma Decision Tree, Random Forest, dan Naïve Bayes untuk Prediksi Banjir di Desa Dayeuhkolot,” TELKA: Jurnal Telekomunikasi, Elektronika, Komputasi dan Kontrol, vol. 9, no. 1, pp. 52–61, May 2023, doi: 10.15575/telka.v9n1.52-61.

C. Y. M. Baidoo, W. Yaokumah, and E. Owusu, “Estimating Overhead Performance of Supervised Machine Learning Algorithms for Intrusion Detection,” International Journal of Information Technologies and Systems Approach, vol. 16, no. 1, pp. 1–19, Feb. 2023, doi: 10.4018/IJITSA.316889.

K. Ghosh and V. Bhattacharjee, “Lung cancer prediction: A performance analysis of machine learning classifiers,” International Journal of Statistics and Applied Mathematics, vol. 9, no. 5, pp. 28–33, Sep. 2024, doi: 10.22271/maths.2024.v9.i5a.1799.

T. Hayashi, T. Shimizu, and Y. Fukami, “Collaborative Problem Solving on a Data Platform Kaggle,” IEICE Tech Report, vol. 120, no. 362, pp. 37–40, Feb. 2021, doi: 10.48550/arXiv.2107.11929.

A. S. Sitio and F. A. Sianturi, “Penerapan Algoritma Machine Learning dalam Analisis Pola Perilaku Penggunaan Internet,” Dike: Jurnal Ilmu Disiplin, vol. 2, no. 2, pp. 46–51, Aug. 2024, doi: 10.69688/dike.v2i2.102.

S. M. Malakouti, M. B. Menhaj, and A. A. Suratgar, “The usage of 10-fold cross-validation and grid search to enhance ML methods performance in solar farm power generation prediction,” Clean Eng Technol, vol. 15, no. 9, pp. 1–7, Jul. 2023, doi: 10.1016/j.clet.2023.100664.

IBM, “What is a Decision Tree | IBM.” Accessed: Feb. 17, 2025. [Online]. Available: https://www.ibm.com/think/topics/decision-trees

A. Jananto, S. Sulastri, E. Nur Wahyudi, and S. Sunardi, “Data Induk Mahasiswa sebagai Prediktor Ketepatan Waktu Lulus Menggunakan Algoritma CART Klasifikasi Data Mining,” Jurnal Sisfokom (Sistem Informasi dan Komputer), vol. 10, no. 1, pp. 71–78, Feb. 2021, doi: 10.32736/sisfokom.v10i1.991.

IBM, “What Is Random Forest? | IBM.” Accessed: Feb. 17, 2025. [Online]. Available: https://www.ibm.com/think/topics/random-forest

R. Rizky Pratama and R. R. Suryono, “Performance Comparison of Naive Bayes, Support Vector Machine and Random Forest Algorithms for Apple Vision Pro Sentiment Analysis,” Jurnal Teknik Informatika (JUTIF), vol. 6, no. 1, pp. 31–40, Feb. 2025, doi: 10.52436/1.jutif.2025.6.1.4035.

S. Sathyanarayanan and B. Roopashri Tantri, “Confusion Matrix-Based Performance Evaluation Metrics,” African Journal of Biomedical Research, vol. 27, no. 4s, pp. 4023–4031, Nov. 2024, doi: 10.53555/AJBR.v27i4S.4345.

A. Reddy Eppa, “A Comparative Study on Efficiency, Effectiveness, and Practical Applications Using the GRA Method,” Journal of Artificial intelligence and Machine Learning, vol. 3, no. 1, pp. 1–12, Jan. 2025, doi: 10.55124/jaim.v3i1.260.

P. Kulkarni and V. Lavanya, “A Comparative Study of Machine Learning Algorithms on Structured Data,” JOURNAL OF EMERGING TECHNOLOGIES AND INNOVATIVE RESEARCH (JETIR), vol. 11, no. 9, pp. d156–d159, Sep. 2024, [Online]. Available: www.jetir.org

C. Brosch, “Influence of Static Code Analysis on Energy Consumption of Software,” in EnviroInfo 2023: Sustainable Software Engineering and Energy Efficiency, Garching, Germany: Gesellschaft für Informatik e.V., Oct. 2023, pp. 111–120. doi: 10.18420/env2023-010.

J. A. Firdaus, A. Setia Budi, and E. Setiawan, “Analisis Performa Algoritma Machine Learning Pada Perangkat Embedded ATmega328P,” Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK), vol. 10, no. 2, pp. 245–254, Apr. 2023, doi: 10.25126/jtiik.2023106196.