ANALISIS MACHINE LEARNING UNTUK PREDIKSI PENYAKIT PARU-PARU MENGGUNAKAN  RANDOM FOREST

Ade Christian; Hariyanto Hariyanto; Ahmad Yani; Sumanto Sumanto

doi:10.47080/iftech.v7i1.3906

Ade Christian Universitas Bina Sarana Informatika
Hariyanto Hariyanto
Ahmad Yani
Sumanto Sumanto Universitas Bina Sarana Informatika

DOI: https://doi.org/10.47080/iftech.v7i1.3906

Keywords: Lung Disease, Machine learning, Orange Software, RF, Random Forest Algorithm

Abstract

Lung diseases, including COPD, lung cancer, and asthma, are serious global health issues, causing over seven million deaths annually. Advanced technologies, such as deep learning and the Random Forest algorithm, have been effectively utilized to detect and classify lung diseases from imaging data with high accuracy. This study aims to demonstrate the effectiveness of Random Forest in predicting lung diseases. The dataset used consists of 30,000 records with 11 attributes, collected from Kaggle and processed using Orange software version 3.36.2. The implementation of the Random Forest algorithm was conducted with 10 decision trees and six attributes considered at each split. The model was tested using Cross Validation with 10 folds. The testing results showed an AUC value of 0.993, indicating a very high level of accuracy. A confusion matrix was used to measure the model's performance through various metrics, including accuracy, precision, recall, F1-score, and AUC. This model achieved high accuracy, with ROC AUC values of 0.453 for predicting the presence of lung disease and 0.547 for predicting its absence. These results confirm that the Random Forest algorithm is an effective predictive tool for identifying lung diseases. This study makes a significant contribution to the development of more accurate and efficient diagnostic techniques, assisting medical professionals in identifying lung diseases in patients. With a deeper understanding of how this algorithm operates in the healthcare domain, it is expected to significantly enhance the quality of patient diagnosis and care.

References

Gould, G. S., Hurst, J. R., Trofor, A., Alison, J. A., Fox, G., Kulkarni, M. M., Wheelock, C. E., Clarke, M., & Kumar, R. (2023). Recognising the importance of chronic lung disease: a consensus statement from the Global Alliance for Chronic Diseases (Lung Diseases group). Respiratory Research, 24(1), 15.
Heitlinger, E. (2023). Globale Belastung durch Lungenkrankheiten bekämpfen. Healthbook TIMES Das Schweizer Ärztejournal Journal Des Médecins Suisses, 7(5–6), 4–5.
Jasmine Pemeena Priyadarsini, M., Kotecha, K., Rajini, G. K., Hariharan, K., Utkarsh Raj, K., Bhargav Ram, K., Indragandhi, V., Subramaniyaswamy, V., & Pandya, S. (2023). Lung diseases detection using various deep learning algorithms. Journal of Healthcare Engineering, 2023(1), 3563696.
Midyanti, D. M., Bahri, S., & Hidayati, R. (2020). Diagnosis of lung disease using Learning Vector Quantization 3 (LVQ3). Scientific Journal of Informatics, 7(2), 174.
Musa, O. R., & Alang, A. (2017). ANALISIS Penyakit Paru-Paru Menggunakan Algoritma K-Nearest Neighbors Pada Rumah Sakit Aloei Saboe Kota Gorontalo. ILKOM Jurnal Ilmiah, 9(3), 348–352.
Prasetyo, T. M., Amrullah, A., Syahrir, S., & Sari, B. N. (2022). Implementasi Algoritma SVM (Support Vector Machine) Dalam Klasifikasi Penyakit Paru-Paru Berdasarkan Fitur Pola Bentuk. Jurnal Teknologi Informasi, 6(1), 1–6.
Putra, B. S. C., Tahyudin, I., Kusuma, B. A., & Isnaini, K. N. (2024). Efektivitas Algoritma Random Forest , XGBoost , dan Logistic Regression dalam Prediksi Penyakit Paru-paru. Techno.Com, 23(4), 909–922.
Sandika, A., Ramadhan, F. R., Iman, I. N., & Jihad, J. (2024). Optimasi Prediksi Penyakit Paru-Paru dan Kanker Paru melalui Integrasi Algoritma Random Forest . BIIKMA : Buletin Ilmiah Ilmu Komputer Dan Multimedia, 2(3), 585–591.
SENDY, H. P. (2023). Evaluasi Kinerja Metode Support Vector Machine (Svm), Naive Bayes Dan Decision Tree Untuk Diagnosa Penyakit Jantung.
Siregar, A. P., Purba, D. P., Pasaribu, J. P., & Bakara, K. R. (2023). Implementasi Algoritma Random Forest Dalam Klasifikasi Diagnosis Penyakit Stroke. Jurnal Penelitian Rumpun Ilmu Teknik, 2(4), 155–164.
Sofyan, F. M. A., Voutama, A., & Umaidah, Y. (2023). Penerapan Algoritma C4. 5 Untuk Prediksi Penyakit Paru-Paru Menggunakan Rapidminer. JATI (Jurnal Mahasiswa Teknik Informatika), 7(2), 1409–1415.
Sriyanto, S., & Supriyatna, A. R. (2023). Prediksi Penyakit Diabetes Menggunakan Algoritma Random Forest . TEKNIKA, 17(1), 163–172.
Swartzendruber, J. A., Nicholson, B. J., & Murthy, A. K. (2020). The role of connexin 43 in lung disease. Life, 10(12), 1–11. https://doi.org/10.3390/life10120363
Utami, N. W., & Saptiari, N. N. (2020). Penerapan Data Mining Untuk Klasifikasi Penyebab Kematian Menggunakan Algoritma Support Vector Machine. Jurnal Ilmiah Ilmu Terapan Universitas Jambi| JIITUJ|, 4(2), 234–240.
Wahid, M. A. R., Nugroho, A., & Anshor, A. H. (2023). Prediksi Penyakit Kanker Paru-Paru Dengan Algoritma Regresi Linier. Bulletin of Information Technology (BIT), 4(1), 63–74.
Yunianto, M., Anwar, F., Septianingsih, D. N., Ardyanto, T. D., & Pradana, R. F. (2021). Klasifikasi Kanker Paru Paru Menggunakan Naïve Bayes Dengan Variasi Filter Dan Ekstraksi Ciri Gray Level Co-Occurance Matrix (GLCM). Indonesian Journal of Applied Physics, 11(2), 256–268.