Lung Cancer Prediction Model using Logistic Linear Regression with Imbalanced Dataset

  • Priscilia Lovita Paelongan Telkom University
  • Irma Palupi
Abstract views: 335 , 616 downloads: 729
Keywords: Lung Cancer, Prediction Model, Logistic Linear Regression

Abstract

Cancer is one of the leading causes of death worldwide. Cancer cases in Indonesia have now reached 4.8 million in 2018. Most cases are breast, cervix, and lung. Furthermore, we need to note that 43 percent of these cancer cases are preventable.

This study uses a linear logistics regression model. Linear logistic regression models can be used for categoric datasets. The appropriate model is obtained after parameter assessment, test the significance of each affecting attribute, and test the suitability of the model. This is done to obtain prediction models and risk factors at the level of correlation of disease size. This method is relatively easy and conceptually practical, so it is possible to apply it to diagnose early symptoms of lung cancer. The results include a linear logistics regression model for early prediction of lung cancer patients based on symptoms, habits, and history of health diseases to see the likelihood that someone with a certain level of risk could have lung cancer. The factors that affect a person with lung cancer are difficulty swallowing, coughing, chronic diseases, fatigue, and age.

Downloads

Download data is not yet available.

References

[1] Kementrian Kesehatan RI. 2015. Situasi Penyakit Kanker. [Online] Available at:https://www.kemkes.go.id/resource/download/
pusdatin/infodatin/infodatin-kanker.pdf
[2] Shen Shuting, Fan Ziqiang, dan Gou Qi. 2017. Design and application of tumor prediction model based on statistical method. Computer Assisted Surgery.
[3] Marcus MW, Chen Y, Raji OY, et al. 2015. LLPi: liverpool lung project risk prediction model for lung cancer incidence. Cancer Prev Res.
[4] Millennia F, Spitz MR, Marek K, et al. 2011. A smoking-based carcinogenesis model for lung cancer risk prediction. Int J Cancer.
[5] Zaen, Nanida Jenahara. 2019. Diagnosis Penyakit Stroke dengan Metode Regresi Logistik Biner. UIN Sunan Ampel Surabaya.
[6] Rubiati, Meita Ariani. 2014. Penerapan Regresi Logistik Biner dan Analisis Dominan untuk Menganalisis Faktor-Faktor yang Berpengaruh terhadap Hipertensi. Institut Pertanian Bogor.
[7] Wihansah, Dinia. 2012. Model Regresi Logistik Biner untuk Mengidentifikasi Faktor-Faktor yang Berpengaruh terhadap Status Anemia pada Ibu Hamil. Institut Pertanian Bogor.
[8] Utomo, Setyo. 2009. Model Regresi Logistik untuk Menunjukkan Pengaruh Pendapatan per Kapita, Tingkat Pendidikan, dan Status Pekerjaan terhadap Status Gizi Masyarakat Kota Surakarta. Universitas Sebelas Maret Surakarta.
[9] Redaksi Halodoc. 2018. Ketahui Perbedaan Tumor Jinak dan Tumor Ganas. [Online] Available at: https://www.halodoc.com/
ketahui-perbedaan-tumor-jinak-dan-tumor-ganas
[10] Afrianto Yudi, Fauzy Muh. Farid, dan Setiawati Agustina. 2014. Kanker Paru. [Online] Available at: https://ccrc.farmasi.ugm.ac.id/?page_id=802
[11] Staceyinrobert. 2017. Survey Lung Cancer. [Online] Available at: https://data.world/sta427ceyin/survey-lung-cancer [Accessed 14 September 2020].
[12] Yuniana, Deva Rizky. 2015. Perbedaan Nilai Alpha Dengan Nilai Signifikansi. [Online] Available at: http://fnistatistics.com/divisi_detail.php?id=114
[13] Nurlaila Dwi, Dadan Kusnandar, Evy Sulistianingsih. 2013. Perbandingan Metode Maximum Likelihood Estimation (MLE) dan Metode Bayes dalam Pendugaan Parameter Distribusi Eksponensial. [Online] Available at: https://core.ac.uk/download/pdf/326807809.pdf
[14] Kusmantoro Zaky Nur, Dr. Danardono, M.P.H., Ph.D. 2018. Akurasi Uji Diagnostik Menggunakan Luasan Bawah Kurva ROC Smoothed Empirical. [Online] Available at: http://etd.repository.ugm.ac.id/home/detail_pencarian/162253
[15] Setiawan, Fajar. 2012. Pemodelan Regresi Binomial Negatif dan Penerapannya. [Online] Available at: http://eprints.uny.ac.id/1413/1/PEMODELAN_REGRESI_BINOMIAL_NEGATIF_DAN_PENERAPANNYA.pdf
Published
2022-08-01
How to Cite
Paelongan, P. L., & Palupi, I. (2022). Lung Cancer Prediction Model using Logistic Linear Regression with Imbalanced Dataset. Indonesia Journal on Computing (Indo-JC), 7(2), 1-14. https://doi.org/10.34818/INDOJC.2022.7.2.616
Section
Computer Science