A Survey on Early detection of Lung Cancer by gene expression profiles using Data Mining Techniques

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2018 by IJETT Journal
Volume-55 Number-1
Year of Publication : 2018
Authors : Mohammed Mekuriya Adem, Aniket Jagtap
DOI :  10.14445/22315381/IJETT-V55P205


Mohammed Mekuriya Adem, Aniket Jagtap "A Survey on Early detection of Lung Cancer by gene expression profiles using Data Mining Techniques", International Journal of Engineering Trends and Technology (IJETT), V55(1),21-24 January 2018. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Lung Cancer is the most common causes of death for human beings all over the world. Early detection and treatment can increase the survival rate of lung cancer patients. This work proposes a methodology that detects lung cancer using gene expression profiles. The proposed system first extracts significant features from the input patterns by using Information Gain (IG). Then the Genetic Algorithm (GA) is applied for feature reduction. The proposed system is evaluated by considering microarray dataset and compared with the most recent systems. Support Vector Machine (SVM) is applied for classification.

[1] Salem, H., Attiya, G., & El-Fishawy, N. (2017). Early diagnosis of breast cancer by gene expression profiles. Pattern Analysis and Applications, 20(2), 567-578.
[2] Ali?kovi?, E., &Subasi, A. (2017). Breast cancer diagnosis using GA feature selection and Rotation Forest. Neural Computing and Applications, 28(4), 753-763.
[3] Nguyen, T., Nahavandi, S., Creighton, D., &Khosravi, A. (2015). Mass spectrometry cancer data classification using wavelets and genetic algorithm. FEBS letters, 589(24), 3879-3886.
[4] Kashyap, A., Gunjan, V. K., Kumar, A., Shaik, F., & Rao, A. A. (2016). Computational and Clinical Approach in Lung Cancer Detection and Analysis. Procedia Computer Science, 89, 528-533.
[5] Wu, W. J., Lin, S. W., & Moon, W. K. (2012). Combining support vector machine with genetic algorithm to classify ultrasound breast tumor images. Computerized Medical Imaging and Graphics, 36 (8), 627-633.
[6] Fernandez-Cuesta, L., Perdomo, S., Avogbe, P. H., Leblay, N., Delhomme, T. M., Gaborieau, V., &Mukeria, A. (2016). Identification of circulating tumor DNA for the early detection of small-cell lung cancer. EBioMedicine, 10, 117-123.
[7]Sudheesh, R. K., Rajan, J., Veena, V. S., &Sujathan, K. (2016, September). Study of malignancy associated changes in sputum images as an indicator of lung cancer. In Technology Symposium (TechSym), 2016 IEEE Students’ (pp. 102-105). IEEE.
[8]Yin, Y., Sedlaczek, O., Muller, B., Warth, A., Gonzalez-Vallinas, M., Grabe, N., ...&Drasdo, D. (2017). Tumor cell load and heterogeneity estimation from diffusion-weighted MRI calibrated with histological data: an example from lung cancer. IEEE Transactions on Medical Imaging.
[9] Zhu, X., Yao, J., Luo, X., Xiao, G., Xie, Y., Gazdar, A., & Huang, J. (2016, April). Lung cancer survival prediction from pathological images and genetic data—An integration study. In Biomedical Imaging (ISBI), 2016 IEEE 13th International Symposium on (pp. 1173-1176). IEEE.
[10] Sun, B., Yue, S., Hao, Z., Cui, Z., Wang, H., & Zhang, W. (2017, May). Early lung cancer identification based on ERT measurements. In Instrumentation and Measurement Technology Conference (I2MTC), 2017 IEEE International (pp. 1-5). IEEE.
[11]Deshmukh, S., &Shinde, S. (2016, September). Diagnosis of Lung Cancer using Pruned Fuzzy Min-Max Neural Network. In Automatic Control and Dynamic Optimization Techniques (ICACDOT), International Conference on (pp. 398-402). IEEE.
[12] Chauhan, D., & Jaiswal, V. (2016, October). An efficient data mining classification approach for detecting lung cancer disease. In Communication and Electronics Systems (ICCES), International Conference on (pp. 1-8). IEEE.
[13] Hawkins, S. H., Korecki, J. N., Balagurunathan, Y., Gu, Y., Kumar, V., Basu, S., ... &Gillies, R. J. (2014). Predicting outcomes of nonsmall cell lung cancer using CT image features. IEEE Access, 2, 1418-1426.
[14] Eskandarian, P., & Bagherzadeh, J. (2015, May). Computer-aided detection of Pulmonary Nodules based on SVM in thoracic CT images. In Information and Knowledge Technology (IKT), 2015 7th Conference on (pp. 1-6). IEEE.
[15] Kurkure, M., &Thakare, A. (2016, August). Lung cancer detection using Genetic approach. In Computing, Communication Control and automation (ICCUBEA), 2016 International Conference on (pp. 1-5). IEEE.
[16] Froz, B. R., de Carvalho Filho, A. O., Silva, A. C., de Paiva, A. C., Nunes, R. A., & Gattass, M. (2017). Lung nodule classification using artificial crawlers, directional texture and support vector machine. Expert Systems with Applications, 69, 176-188.
[17] Yin, G., Li, C., Chen, H., Luo, Y., Orlandini, L. C., Wang, P., & Lang, J. (2017). Predicting brain metastases for non-small cell lung cancer based on magnetic resonance imaging. Clinical & experimental metastasis, 34(2), 115-124.
[18] Al-Rajab, M., Lu, J., & Xu, Q. (2017). Examining applying high performance genetic data feature selection and classification algorithms for colon cancer diagnosis. Computer Methods and Programs in Biomedicine, 146, 11-24.
[19] Saini, A. K., Bhadauria, H. S., & Singh, A. (2016, February). A Survey of Noise Removal Methodologies for Lung Cancer Diagnosis. In Computational Intelligence & Communication Technology (CICT), 2016 Second International Conference on (pp. 673-678). IEEE.
[20] Kureshi, N., Abidi, S. S. R., &Blouin, C. (2016). A predictive model for personalized therapeutic interventions in non-small cell lung cancer. IEEE journal of biomedical and health informatics, 20 (1), 424-431.
[21]Pengo, T., Muñoz-Barrutía, A., & Ortiz-de-Solorzano, C. (2014). A Novel Automated Microscopy Platform for Multiresolution Multispectral Early Detection of Lung Cancer Cells in Bronchoalveolar Lavage Samples. IEEE Systems Journal, 8 (3), 985-994.
[22] Melissa, C.S.:„Lungcancer?(Medicine.net, 2011).
[23] Azzawi, H., Hou, J., Xiang, Y., &Alanni, R. (2016). Lung cancer prediction from microarray data by gene expression programming. IET systems biology, 10(5), 168-178.

Lung Cancer, Genetic Algorithm, Information Gain, Support Vector Machine, Feature Selection.