Implementasi Decision tree Untuk Prediksi Kanker Paru-Paru

F Faurika(1), Ahsanun Naseh Khudori(2*), M Syauqi Haris(3),

(1) Institut Teknologi, Sains, dan Kesehatan RS.DR. Soepraoen Kesdam V/BRW, Indonesia
(2) Institut Teknologi, Sains, dan Kesehatan RS.DR. Soepraoen Kesdam V/BRW, Indonesia
(3) Institut Teknologi, Sains, dan Kesehatan RS.DR. Soepraoen Kesdam V/BRW, Indonesia
(*) Corresponding Author

Abstract


Lung cancer is a disorder of the lungs due to changes in respiratory tract epithelial cells which cause uncontrolled cell division and growth. Lung cancer is caused by several factors such as radiation exposure, smoking, heredity, gender, air pollution, and unhealthy lifestyles. Lung cancer can be detected when the cancer has entered an advanced stage. The large amount of lung cancer diagnosis data currently available can be used to predict lung cancer based on patterns in the data. One of the results of technological advances that can learn patterns in data is machine learning, which has currently made many positive contributions in the health sector. This research aims to predict lung cancer using a decision tree algorithm. This research produces rules based on decision trees which are built and then evaluated to produce the same accuracy, precision, recall, and F1-Score of 100%.


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References


M. Abdul, R. Wahid, A. Nugroho, and A. H. Anshor, “Prediksi Penyakit Kanker Paru-Paru Dengan Algoritma Regresi Linier,” Bull. Inf. Technol., vol. 4,

no. 1, pp. 63–74, 2023.

WHO, “Lung cancer,” 2022, [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/lung-cancer

American Lung Association, “State of Lung Cancer: Texas,” 2021, [Online]. Available: https://www.lung.org/research/state-of-lung-cancer/states/texas

Globocan, “Cancer in Indonesia,” JAMA J. Am. Med. Assoc., vol. 247, no. 22, pp. 3087–3088, 2020, doi: 10.1001/jama.247.22.3087.

I. Buana and D. A. Harahap, “Asbestos, Radon Dan Polusi Udara Sebagai Faktor Resiko Kanker Paru Pada Perempuan Bukan Perokok,” AVERROUS J. Kedokt. dan Kesehat. Malikussaleh, vol. 8, no. 1, p. 1, 2022, doi: 10.29103/averrous.v8i1.7088.

D. Septhya et al., “Implementasi Algoritma Decision Tree dan Support Vector Machine untuk Klasifikasi Penyakit Kanker Paru,” MALCOM Indones. J. Mach. Learn. Comput. Sci., vol. 3, no. 1, pp. 15–19, 2023.

J. Sinurat, “Jaringan Saraf Tiruan Diagnosa Penyakit Kanker Paru-Paru Menggunakan Metode Hebb Rule,” Bull. Inf. Technol., vol. 2, no. 1, pp. 20–27, 2021.

Rokom, “One Stop Service, Deteksi Dini Kanker Paru di RSUP Persahabatan,” 2023, [Online]. Available: https://sehatnegeriku.kemkes.go.id/baca/rilis-media/20230405/3242727/one-stop-service-deteksi-dini-kanker-paru-di-rsup-persahabatan/

N. R. Muntiari and K. H. Hanif, “Klasifikasi Penyakit Kanker Payudara Menggunakan Perbandingan Algoritma Machine Learning,” J. Ilmu Komput. dan Teknol., vol. 3, no. 1, pp. 1–6, 2022, doi: 10.35960/ikomti.v3i1.766.

R. A. Sowah, A. A. Bampoe-Addo, S. K. Armoo, F. K. Saalia, F. Gatsi, and B. Sarkodie-Mensah, “Design and Development of Diabetes Management System Using Machine Learning,” Int. J. Telemed. Appl., vol. 2020, 2020, doi: 10.1155/2020/8870141.

S. Ghosh, A. Dasgupta, and A. Swetapadma, “A study on support vector machine based linear and non-linear pattern classification,” Proc. Int. Conf. Intell. Sustain. Syst. ICISS 2019, no. Iciss, pp. 24–28, 2019, doi: 10.1109/ISS1.2019.8908018.

A. Septhiani, “Analisis Perbandingan Algoritma Supervised Learning untuk Prediksi Kasus Covid-19 di Jakarta,” vol. 7, no. September, pp. 583–594, 2023.

S. Saeed, A. Abdullah, N. Jhanjhi, and T. Malaysia, “Analysis of the Lung Cancer patient’s for Data Mining Tool,” IJCSNS Int. J. Comput. Sci. Netw. Secur., vol. 19, no. 7, p. 90, 2019.

Kumar Mohan and Bhraguram Thayyil, “Machine Learning Techniques for Lung Cancer Risk Prediction using Text Dataset,” Int. J. Data Informatics Intell. Comput., vol. 2, no. 3, pp. 47–56, 2023, doi: 10.59461/ijdiic.v2i3.73.

T. Gupta, T. Qawasmeh, and S. McCalla, “Predictions of Programmed Cell Death Ligand 1 Blockade Therapy Success in Patients with Non-Small-Cell Lung Cancer,” BioMedInformatics, vol. 3, no. 4, pp. 1060–1070, 2023, doi: 10.3390/biomedinformatics3040063.

Juwita, N. Amalita, and M. D. Parma, “Faktor-Faktor Risiko yang Mempengaruhi Kanker Paru-Paru dengan Menggunakan Analisis Regresi Logistik,” UNPjoMath, vol. 4, no. 1, pp. 38–42, 2021, [Online]. Available: https://ejournal.unp.ac.id/students/index.php/mat/article/download/11550/4620

S. S. A.-N. Ibrahim M. Nasser, “Lung Cancer Detection Using Artificial Neural Network,” vol. 3, no. 3, pp. 17–23, 2019.

L. Wheless, J. Brashears, and A. J. Alberg, “Epidemiology of lung cancer,” Lung Cancer Imaging, pp. 1–15, 2021, doi: 10.1007/978-1-60761-620-7_1.

Z. Bing, Z. Zheng, and J. Zhang, “Risk factors influencing chemotherapy compliance and survival of elderly patients with non-small cell lung cancer,” Afr. Health Sci., vol. 23, no. 3, pp. 291–300, 2023, doi: 10.4314/ahs.v23i3.35.

Q. Aini, N. Lutfiani, H. Kusumah, and M. S. Zahran, “Deteksi dan Pengenalan Objek Dengan Model Machine Learning: Model Yolo,” CESS (Journal Comput. Eng. Syst. Sci., vol. 6, no. 2, p. 192, 2021, doi: 10.24114/cess.v6i2.25840.

M. Idris, R. I. Adam, Y. Brianorman, R. Munir, and D. Mahayana, “Kebenaran dalam Perspektif Filsafat Ilmu Pengetahuan dan Implementasi dalam Data Science dan Machine Leaning,” J. Filsafat Indones., vol. 5, no. 2, pp. 173–181, 2022, doi: 10.23887/jfi.v5i2.42207.

N. Wiranda, H. S. Purba, and R. A. Sukmawati, “Survei Penggunaan Tensorflow pada Machine Learning untuk Identifikasi Ikan Kawasan Lahan Basah,” IJEIS (Indonesian J. Electron. Instrum. Syst., vol. 10, no. 2, p. 179, 2020, doi: 10.22146/ijeis.58315.

R. R. Pratama, “Analisis Model Machine Learning Terhadap Pengenalan Aktifitas Manusia,” vol. 19, no. 2, pp. 302–311, 2020.

A. Saputra, U. Nahdlatul, U. Sidoarjo, and K. Sidoarjo, “KLASIFIKASI PENGENALAN BUAH MENGGUNAKAN ALGORITMA NAIVE,” vol. 2, no. 2, pp. 83–88, 2019.

M. A. Rosid, A. S. Fitrani, Y. Findawati, S. Winata, and V. A. Firmansyah, “Classification of Dengue Hemorrhagic Disease Using Decision Tree with Id3 Algorithm,” J. Phys. Conf. Ser., vol. 1381, no. 1, 2019, doi: 10.1088/1742-6596/1381/1/012039.

S. B. Begenova and T. V. Avdeenko, “Building of fuzzy decision trees using ID3 algorithm,” J. Phys. Conf. Ser., vol. 1015, no. 2, 2018, doi: 10.1088/1742-6596/1015/2/022002.

A. Rajeshkanna and K. Arunesh, “ID3 Decision Tree Classification: An Algorithmic Perspective based on Error rate,” Proc. Int. Conf. Electron. Sustain. Commun. Syst. ICESC 2020, no. Icesc, pp. 787–790, 2020, doi: 10.1109/ICESC48915.2020.9155578.

E. E. Ogheneovo and P. A. Nlerum, “Iterative Dichotomizer 3 (ID3)

Decision Tree: A Machine Learning Algorithm for Data Classification and Predictive Analysis,” Int. J. Adv. Eng. Res. Sci., vol. 7, no. 4, pp. 514–521, 2020, doi: 10.22161/ijaers.74.60.

P. Sathiyanarayanan, S. Pavithra, M. Sai Saranya, and M. Makeswari, “Identification of breast cancer using the decision tree algorithm,” 2019 IEEE Int. Conf. Syst. Comput. Autom. Networking, ICSCAN 2019, pp. 1–6, 2019, doi: 10.1109/ICSCAN.2019.8878757.




DOI: http://dx.doi.org/10.30645/jurasik.v9i1.717

DOI (PDF): http://dx.doi.org/10.30645/jurasik.v9i1.717.g692

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