Issue

Vol. 5, No. 1, February 2020

An application of ANFIS for Lung Diseases Early Detection System

https://doi.org/10.22219/kinetik.v5i1.996
Mochamad Yusuf Santoso
Shipbuilding Institute of Polytechnic Surabaya
Am Maisarah Disrinama
Shipbuilding Institute of Polytechnic Surabaya
Haidar Natsir Amrullah
Shipbuilding Institute of Polytechnic Surabaya

Corresponding Author(s) : Mochamad Yusuf Santoso

yusuf.santoso@ppns.ac.id

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 5, No. 1, February 2020

Abstract

Indonesian Basic Health Research in 2018 showed the prevalence of pneumonia, pulmonary tuberculosis (TB) and lung cancer in Indonesia 4.0% 0.4% and 0.18%, respectively. However, the number of lung specialists is small. According to the Indonesian Lung Specialist Association webpage, the number of doctors joined in the association up to 2008 were 452. This amount is very less when compared with existing lung disease cases. Thus, the handling of lung disease will be too late. The use of ANFIS for early detection of lung disease is growing. However, the systems designed are need preprocessing data to be executed and still applied for one type of disease. This paper will design a desktop application based on ANFIS expert system to detect lung disease early, i.e. for pneumonia, pulmonary TB and lung cancer. The system will work based on simple symptoms expressed by the patient. Subtractive clustering is used for clustering process. The results of the training showed that the models were able to give better performance compared to the model built using conventional clustering methods. The test results show that those three models have comparable performance compared to their counterpart. Software validation shows that the it gives 94.00% succeed for training data and up to 100% for testing data. This application is not intended to replace the role of a doctor, but to help diagnose the patient's condition earlier.

Keywords

ANFIS Early Detection Lung Cancer Pneumonia Tuberculosis
Santoso, M. Y., Disrinama, A. M., & Amrullah, H. N. (2020). An application of ANFIS for Lung Diseases Early Detection System. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 5(1), 29-36. https://doi.org/10.22219/kinetik.v5i1.996
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References
  1. Badan Penelitian dan Pengembangan Kesehatan Kementrian Kesehatan RI, “Hasil Utama Riset Kesehatan Dasar,” 2018.
  2. D. Purnamasari, “The Emergence of Non-communicable Disease in Indonesia,” Acta Med. Indones., vol. 50, no. 4, pp. 273–274, 2018.
  3. M. Yunus and S. Setyowibowo, “Aplikasi sistem pendukung keputusan diagnosa penyakit paru- paru dengan metode forward chaining,” J. Teknol. Inf. Teor. Konsep, dan Implementasi, vol. 2, no. 2, pp. 95–114, 2011.
  4. R. Rahmadewi and R. Kurnia, “Klasifikasi penyakit paru berdasarkan citra rontgen dengan metoda segmentasi sobel,” J. Nas. Tek. Elektro, vol. 5, no. 1, pp. 7–12, 2016. https://doi.org/10.20449/jnte.v5i1.174
  5. I. T. Dessetiadi, A. Pujianto, and M. G. Ardi, “Sistem Pakar Untuk Mendiagnosa Penyakit Paru-Paru,” in Seminar Nasional Teknologi Informasi dan Multimedia 2016, 2016, pp. 3.4-25-3.4-30.
  6. http://www.klikpdpi.com/index.php?mod=content&sel=18
  7. P. Utomo, Wiharto, and E. Suryani, “Sistem Diagnosa Penyakit Paru Berdasarkan Foto Rontgen Dengan Pendekatan Fuzzy Learning Vector Quantization,” J. Teknol. Inf. ITSmart, vol. 1, no. 2, pp. 102–106, 2016. https://doi.org/10.20961/its.v1i2.604
  8. R. Fauzan and A. V. Prananda, “Expert System for Diagnosing Palm Tree Diseases and Pests using Forward Chaining and Certainty Factor,” Kinet. Game Technol. Inf. Syst. Comput. Network, Comput. Electron. Control, vol. 3, no. 1, pp. 27–34, 2018. https://doi.org/10.22219/kinetik.v3i1.524
  9. Asnawati, R. P. Bendriyanti, and H. Aspriono, “Sistem Pakar Mendeteksi Penyakit Asma Pada Puskesmas Lingkar Timur Bengkulu,” J. Media Infotama, vol. 9, no. 2, pp. 162–205, 2013.
  10. A. Saputra, “Pengembangan Sistem Pakar Identifikasi Penyakit Paru-Paru Menggunakan Metode Certainty Factor,” IT J., vol. 4, no. 2, pp. 109–120, 2017.
  11. A. R. M. Al-shamasneh and U. H. B. Obaidellah, “Artificial Intelligence Techniques for Cancer Detection and Classification : Review Study,” Eur. Sci. J., vol. 13, no. 3, pp. 342–370, 2017. https://doi.org/10.19044/esj.2017.v13n3p342
  12. F. Wanita, “Sistem pakar deteksi dini penyakit dengan gejala sesak nafas menggunakan metode forward chaining,” in Prosiding Seminar Nasional Ilmu Komputer dan Teknologi Informasi, 2017, vol. 2, no. 2, pp. 74–79.
  13. R. Kurnia, R. Rahmadewi, and F. Aini, “Deteksi Dini Penyakit Paru Dengan Metoda Bayesian,” in National Conference of Applied Sciences, Engineering, Business and Information Technology, 2016, pp. 15–16.
  14. M. M. Mehdy, P. Y. Ng, E. F. Shair, N. I. Saleh, and C. Gomes, “Artificial Neural Networks in Image Processing for Early Detection of Breast Cancer,” Comput. Math. Methods Med., vol. 2017, 2017. https://doi.org/10.1155/2017/2610628
  15. R. Palaniappan, K. Sundaraj, and F. G. Nabi, “An Overview of Breath Phase Detection – Techniques & Applications,” J. Telecommun. Electron. Comput. Eng., vol. 10, no. 2, pp. 33–36, 2015.
  16. R. K. Purwar and V. Srivastava, “Recent Advancements in Detection of Cancer Using Various Soft Computing Techniques for MR Images,” in Progress in Advanced Computing and Intelligent Engineering, 2018, pp. 99–108. https://doi.org/10.1007/978-981-10-6872-0_10
  17. P. Zarbakhsh, A. Abdoljalil, and D. Hasan, “Early detection of breast cancer using optimized ANFIS and features selection,” in 9th International Conference on Computational Intelligence and Communication Networks, 2017, pp. 39–42. https://doi.org/10.1109/cicn.2017.8319352
  18. K. Jinsa and G. K., “Lung cancer classification using fuzzy logic for CT images,” Int. J. Med. Eng. Informatics, vol. 7, no. 3, pp. 233–249, 2015. https://doi.org/10.1504/ijmei.2015.070128
  19. M. Lavanya and P. M. Kannan, “Lung cancer segmentation and diagnosis of lung cancer staging using MEM (modified expectation maximization) algorithm and artificial neural network fuzzy inference system (ANFIS),” Biomed. Res. An Int. J. Med. Sci., vol. 29, no. 14, 2018. https://doi.org/10.4066/biomedicalresearch.29-18-740
  20. M. Obayya and M. Ghandour, “Lung Cancer Recognition using Radon Transform and Adaptive Neuro Fuzzy Inference System,” Int. J. Comput. Appl., vol. 124, no. 2, pp. 25–30, 2015. https://doi.org/10.5120/ijca2015905373
  21. B. Ashadevi, P. M. Selvi, and B. S. Revathi, “An Effective Diagnosis of Pulmonary Tuberculosis using K-Means Clustering and ANFIS,” Sch. J. Eng. Technol., vol. 5, no. 8, pp. 427–439, 2017.
  22. S. K. Meenakshi and R. C.S., “An Efficient ANFIS based Approach for Screening of Chronic Obstructive Pulmonary Disease (COPD) from Chest CT Scans with Adaptive Median Filtering,” Asian J. Sci. Res., vol. 7, no. 1, pp. 18–32, 2014. https://doi.org/10.3923/ajsr.2014.18.32
  23. S. Smilji et al., “Differences and similarities between the symptoms and clinical signs in patients with pulmonary tuberculosis and pneumonia,” Mil. Med. Pharm. J. Serbia, vol. 76, no. 2, pp. 192–201, 2019. https://doi.org/10.2298/vsp170301080s
  24. C. E. Mosher, M. A. Ott, N. Hanna, S. I. Jalal, and V. L. Champion, “Coping with Physical and Psychological Symptoms: A Qualitative Study of Advanced Lung Cancer Patients and their Family Caregivers,” Support Care Cancer, vol. 23, no. 7, pp. 2053–2060, 2016. https://doi.org/10.1007/s00520-014-2566-8
  25. N. Walia, H. Sing, and A. Sharma, “ANFIS : Adaptive Neuro-Fuzzy Inference System- A Survey,” Int. J. Comput. Appl., vol. 123, no. 13, pp. 32–38, 2015. https://doi.org/10.5120/ijca2015905635
  26. J. S. Jang, “ANFIS : Adaptive Network-Based Fuzzy Inference System,” IEEE Trans. Syst. Man. Cybern., vol. 23, no. 3, pp. 1–21, 1993. https://doi.org/10.1109/21.256541
  27. S. Chiu, “Method and software for extracting fuzzy classification rules by subtractive clustering,” in Proceedings of North American Fuzzy Information Processing, 1996, pp. 461–465. https://doi.org/10.1109/nafips.1996.534778
  28. S. Dalecky and F. V. Zboril, “An Approach to ANFIS Performance,” in Advances in Intelligent Systems and Computing, 378th ed., Springer, Cham, 2015. https://doi.org/10.1007/978-3-319-19824-8_16
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Author Biographies

Mochamad Yusuf Santoso, Shipbuilding Institute of Polytechnic Surabaya
Occupational Health and Safety Engineering
Am Maisarah Disrinama, Shipbuilding Institute of Polytechnic Surabaya
Occupational Health and Safety Engineering
Haidar Natsir Amrullah, Shipbuilding Institute of Polytechnic Surabaya
Occupational Health and Safety Engineering
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