ANALYSIS OF CLASSIFICATION OF LUNG CANCER USING THE DECISION TREE CLASSIFIER METHOD
DOI:
https://doi.org/10.34012/jurnalsisteminformasidanilmukomputer.v7i1.4136Abstract
The International Agency for Research on Cancer (IARC) revealed staggering figures, with 19.3 million global cancer cases and 10 million related deaths in that year. Cancer, characterized by abnormal cell growth, can potentially be dangerous with the ability to metastasize. Notably, lung cancer is often detected in an advanced stage due to a lack of awareness and comprehensive medical assessment. Lung cancer usually presents with a late-stage diagnosis. From 60% to 85% of individuals diagnosed with lung cancer show a lack of awareness about their condition. Early diagnosis using an accurate classification method can significantly increase the success of lung cancer diagnosis. To improve predictions, Decision Tree Classifier method was used in lung cancer classification, resulting in a significant increase in accuracy. This study achieved a good level of accuracy, with an accuracy value of 95.16% at a max_depth model depth of 15, and tested in 40 experimental iterations. These results are expected to provide hope for progress in the classification of lung cancer.
Keywords: Lung, Cancer, Classification, Decision Tree
References
“GLOBOCAN 2020: DataNew Global Cancer | UICC.” https://www-uicc-org.translate.goog/news/globocan-2020-new-global-cancer-data?_x_tr_sl=en&_x_tr_tl=id&_x_tr_hl=id&_x_tr_pto=tc (accessed Aug. 09, 2023).
PPurnamawati, C. Tandrian, EM Sumbayak, and W. Kertadjaja, "Analysis of Primary Lung Cancer Incidence in Indonesia in 2014-2019 Purnamawati1," J. Kedokt. Meditek, vol. 27, no. 2, pp. 164–172, 2021, doi: 10.36452/jkdoktmeditek.v27i2.2066.
N.Kamisi and R. Ratianingsih, "Analysis of the Stability of Mathematical Models for the Spread of Smoking Behavior with Risk Factors for Lung Cancer," J. Ilm. Matt. And Applied., vol. 19, no. 1, pp. 72–81, 2022, doi: 10.22487/2540766x.2022.v19.i1.15710.
V. No, E. April, and EMIHandrina, “RISK FACTORS OF BREAST CANCER IN ASIA (WITH A META ANALYSIS STUDY APPROACH) ALFITA,” vol. 4, no. 3, pp. 304–312, 2022, [Online]. Available: https://jurnal.ensiklopediaku.org/ojs-2.4.8-3/index.php/ensiklopedia/article/view/501
Juwita, N. Amalita, and MD Parma, "Risk Factors Affecting Lung Cancer Using Logistic Regression Analysis," UNPjoMath, vol. 4, no. 1, pp. 38–42, 2021.
K.Lungs, "A Comparative Analysis of Filter Variations in Edge Detection Using the Canny Method for Ct-Scan Images," vol. 8, no. 2, pp. 77–81, 2023.
E. Tiana and S.Wahyuni, "Results of Analysis of Data Mining Techniques with the Naive Bayes Method for Diagnosing Breast Cancer," J. Sist. Computer. and Inform., vol. 1, no. 2, p. 130, 2020, doi: 10.30865/json.v1i2.1766.
MsNaezer, "Naïve Bayes Algorithm Performance Analysis and k-NN for Predicting Lung Cancer," J. Ilm. Computing, vol. 4, no. 1, pp. 88–100, 2023.
L. Sari, A.Romadloni, and R. Listyaningrum, "Application of Data Mining in Lung Cancer Prediction Analysis Using the Random Forest Algorithm," Infotekmesin, vol. 14, no. 1, pp. 155–162, 2023, doi: 10.35970/infotekmachine.v14i1.1751.
D.Septhya, K. Rahayu, S. Rabbani, and V. Fitria, “Implementation of Decision Tree Algorithm and Support Vector Machine for Lung Cancer Classification,” vol. 3, no. April, pp. 15–19, 2023.
vvPermana, HN Fazri, MFN Athoilah, M. Robi, and R. Firmansyah, "Application of Data Mining in Lung Cancer Prediction Analysis Using the Random Forest Algorithm," J. Ilm. Tech. inform. and Commun., vol. 3, no. 2, pp. 27–41, 2023, doi: doi.org/10.55606/juitik.v3i2.472.
“1.10. Decision Trees — scikit-learn 1.3.0 documentation.” https://scikit-learn.org/stable/modules/tree.html (accessed Aug. 09, 2023).
E. Indra et al., “A Comparison of Heart Abnormalities Detection on ECG using KNN and Decision Tree,” InternetworkingIndonesia. J., vol. 12, no. 2, pp. 19–23, 2020.
AROktavyani et al., "Comparison of the Naive Bayes, K-NN and Decision Tree Methods to the Healthcare Stroke Dataset", doi: 10.31284/p.snestik.2023.4067.
A. Wahab, S.Samarinda, I. Lishania, R. Goejantoro, and YN Nasution, "Comparison of the Naive Bayes Classification Method and the Algorithmic Decision Tree Method (J48) in Stroke Patients at Abdul Wahab Sjahranie Hospital Samarinda Hospital," J. EXPONENTIAL, vol. 10, no. 2, 2019.
“Lung Cancer | Kaggle." https://www.kaggle.com/datasets/mysarahmadbhat /lung-cancer (accessed Aug. 09, 2023).
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 wendy setiawan, Jepri Banjarnahor, Muhammad Faja Shandika
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish their manuscripts through the Journal of Information Systems and Computer Science agree to the following:
- Copyright to the manuscripts of scientific papers in this Journal is held by the author.
- The author surrenders the rights when first publishing the manuscript of his scientific work and simultaneously the author grants permission / license by referring to the Creative Commons Attribution-ShareAlike 4.0 International License to other parties to distribute his scientific work while still giving credit to the author and the Journal of Information Systems and Computer Science as the first publication medium for the work.
- Matters relating to the non-exclusivity of the distribution of the Journal that publishes the author's scientific work can be agreed separately (for example: requests to place the work in the library of an institution or publish it as a book) with the author as one of the parties to the agreement and with credit to sJournal of Information Systems and Computer Science as the first publication medium for the work in question.
- Authors can and are expected to publish their work online (e.g. in a Repository or on their Organization's/Institution's website) before and during the manuscript submission process, as such efforts can increase citation exchange earlier and with a wider scope.
