Dergiler / Turkish Journal of Engineering / 2023 / Cilt: 7 Sayı: 4

Machine learning-based lung cancer diagnosis

Sayfa
322–330
Yayın Tarihi
2023-10-05
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

The results obtained show that the proposed model can detect cancer with a maximum accuracy of 91%.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Cancer is one of the leading health problems, occurring in various organs and tissues of the body, and its incidence is increasing worldwide.
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Temel Bulgular & Veri:Due to its worldwide prevalence, increasing number of cases, and deadly consequences, early detection of lung cancer, as with all other cancers, greatly increases the chances of survival.
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Sonuç & Katkı:The results obtained show that the proposed model can detect cancer with a maximum accuracy of 91%.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Lung cancer · Early diagnosis · Machine learning

Abstract

Cancer is one of the leading health problems, occurring in various organs and tissues of the body, and its incidence is increasing worldwide. Lung cancer is one of the deadliest types of cancer. Due to its worldwide prevalence, increasing number of cases, and deadly consequences, early detection of lung cancer, as with all other cancers, greatly increases the chances of survival. As with all other diseases, the diagnosis of cancer is only possible after the appearance of various symptoms and an examination by specialists. Known symptoms of lung cancer are shortness of breath, coughing, wheezing, jaundice in the fingers, chest pain, and difficulty swallowing. The diagnosis is made by an expert on site based on these symptoms and additional tests. The aim of this study is to detect the disease at an earlier stage based on the symptoms present, to assess more cases with less time and cost, and to achieve results in new situations that are as successful or even faster than those of human experts by deriving them from existing data using different algorithms. The aim is to develop an automated model that can detect early-stage lung cancer based on machine learning methods. The developed model includes nine different machine learning algorithms (NB, LR, DT, RF, GB, and SVM). The success of the classification algorithms used was evaluated using the metrics of accuracy, sensitivity, and precision calculated using the parameters of the confusion matrix. The results obtained show that the proposed model can detect cancer with a maximum accuracy of 91%.

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Bu Makaleye Atıf Yap

DİRİK, M. (2023). Machine learning-based lung cancer diagnosis. Turkish Journal of Engineering, Cilt: 7 Sayı: 4, 322–330. https://ajindex.com/articles/950912

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Kaynakça (9)

  1. Xie, Y., Meng, W. Y., Li, R. Z., Wang, Y. W., Qian, X., Chan, C.,. & Leung, E. L. H. (2021). Early lung cancer diagnostic biomarker discovery by machine learning methods. Translational oncology, 14(1), 100907. https://doi.org/10.1016/j.tranon.2020.100907
  2. Chiu, H. Y., Chao, H. S., & Chen, Y. M. (2022). Application of artificial intelligence in lung cancer. Cancers, 14(6), 1370. https://doi.org/10.3390/cancers14061370
  3. Masud, M., Sikder, N., Nahid, A. A., Bairagi, A. K., & AlZain, M. A. (2021). A machine learning approach to diagnosing lung and colon cancer using a deep learning-based classification framework. Sensors, 21(3), 748. https://doi.org/10.3390/s21030748 https://www.mohw.gov.tw/cp-4650-50697-2.html https://www.who.int/news-room/fact-sheets/detail/cancer
  4. Sung, H., Ferlay, J., Siegel, R. L., Laversanne, M., Soerjomataram, I., Jemal, A., & Bray, F. (2021). Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians, 71(3), 209-249. https://doi.org/10.3322/CAAC.21660 https://gco.iarc.fr/ https://www.who.int/news-room/fact-sheets/detail/cancer
  5. Rock, C. L., Thomson, C., Gansler, T., Gapstur, S. M., McCullough, M. L., Patel, A. V.,. & Doyle, C. (2020). American Cancer Society guideline for diet and physical activity for cancer prevention. CA: a cancer journal for clinicians, 70(4), 245-271. https://doi.org/10.3322/CAAC.21591
  6. Shakeel, P. M., Tolba, A., Al-Makhadmeh, Z., & Jaber, M. M. (2020). Automatic detection of lung cancer from biomedical data set using discrete AdaBoost optimized ensemble learning generalized neural networks. Neural Computing and Applications, 32, 777-790. https://doi.org/10.1007/S00521-018-03972-2/FIGURES/8
  7. Bruno, F., Granata, V., Cobianchi Bellisari, F., Sgalambro, F., Tommasino, E., Palumbo, P.,. & Barile, A. (2022). Advanced Magnetic Resonance Imaging (MRI) Techniques: Technical Principles and Applications in Nanomedicine. Cancers, 14(7), 1626. https://doi.org/10.3390/CANCERS14071626
  8. Zhang, Y., Wang, R., Hu, J., Qin, X., Chen, A., & Li, X. (2022). Magnetic resonance imaging (MRI) and computed topography (CT) analysis of Schatzker type IV tibial plateau fracture revealed possible mechanisms of injury beyond varus deforming force. Injury, 53(2), 683-690. https://doi.org/10.1016/J.INJURY.2021.09.041
  9. Grootjans, W., Rietbergen, D. D., & van Velden, F. H. (2022, May). Added value of respiratory gating in positron emission tomography f