پیش‌بینی بیماری سل در منطقه‌ی سیستان با استفاده از روش جنگل تصادفی
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چکیده
مقدمه: بیماری سل، یكی از مهم‌ترین بیماری‌های عفونی قرن حاضر می‌باشد كه توانایی درگیر نمودن كلیه‌ی ارگان‌های بدن را دارد ولی ریه‌ها بیشتر به سل مبتلا می‌شوند. در منطقه‌ی سیستان، سوء تغذیه و همجواری با کشورهای پرخطر، دو عامل عمده برای شیوع بالای بیماری سل می‌باشد. به همین دلیل پیش‌بینی و تشخیص به موقع بیماری سل برای کمک به بیمار و جلوگیری از انتقال آن به سایرین از اهمیت ویژه‌ای برخوردار است. لذا این پژوهش با هدف تسهیل در پیش‌بینی بیماری از روش جنگل تصادفی استفاده کرده است.
شیوه‌ی مطالعه: داده‌های مورد استفاده در این پژوهش، از اطلاعات فرم بیماریابی موارد مشکوک مراکز خدمات بهداشتی و درمانی شهرستان‌های زابل، نیمروز و هامون، طی سال‌های 94 تا 97 گردآوری شد. سپس برای پیش‌بینی بیماری سل از روش جنگل تصادفی در نرمافزار R استفاده گردید.
یافته‌ها: مرور اولیه‌ی داده‌ها نشان داد که از مجموع 116 فرم بیماریابی موجود مربوط به بیماران مبتلا به سل شناسایی شده، 58 نفر دارای سل ریوی اسمیر مثبت، 36 نفر سل ریوی اسمیر منفی و 22 نفر دارای سل خارج ریوی بودند. نتایج تحلیل نشان داد دقت نرمافزار برای پیش‌بینی سل در این سه گروه، 57 درصد بوده که با حذف موارد سل خارج ریوی به 68 درصد افزایش یافت.
نتیجه‌گیری: پیش‌بینی موارد سل خارج ریوی با استفاده از داده‌های فرم بیماریابی دشوار است. همچنین برای افزایش دقت پیش‌بینی موارد سل ریوی اسمیر مثبت و اسمیر منفی توسط روش جنگل تصادفی، باید عوامل خطر فرم بیماریابی مورد بازنگری قرار گیرند.
کلمات کلیدی: پیش‌بینی سل، جنگل تصادفی، سیستان
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استناد به مقاله
احمدیر., پناهی میشکارا., گلویز., & پردلی شهرکیا. (2020). پیش‌بینی بیماری سل در منطقه‌ی سیستان با استفاده از روش جنگل تصادفی. مجله دانشکده پزشکی زابل, 53-60. Retrieved از http://jzms.ir/index.php/JZMS/article/view/180
نوع مقاله
مقاله پژوهشی
مراجع
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35. Asha T, Natarajan S, Murthy KNB. Effective classification algorithms to predict the accuracy of tuberculosis - A machine learning approach. Int J Com Science & Inf Security 2011; 9(7): 89-94.
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37. Klotz A, Harouna A, Smith AF. Forecast analysis of the incidence of tuberculosis in the province of Quebec. BMC Public Health 2013; 13: 400.
38. Cao S, Wang F, Tam W, Tse LA, Kim JH, Liu J, et al. A hybrid seasonal prediction model for tuberculosis incidence in China. BMC Med Inform Decis Mak 2013; 13: 56-63.
39. Daliri Shamsabadi H, Ebrahimpour Koumleh H. Predicting tuberculosis using smart data mining algorithms. Proceedings of the National Conference on Computer Engineering and Information Technology, Shoshtar, Iran: 2013 Mar. [In Persian].
2. Denkinger CM, Kik SV, Cirillo DM, Casenghi M, Shinnick T, Weyer K, et al. Defining the needs for next generation assays for tuberculosis. J Infect Dis 2015; 211(Suppl 2): S29-S38.
3. Arsang SH, Kazemnejad A, Amani F. Epidemiology of tuberculosis in Iran (2001-8). J Gorgan Univ Med Sci 2011; 13(3): 78-86.
4. Tabatabayi M, Zahrayi M, Ahmad-Nia H, Ghotbi M, Rahimi F, Gooya M. Principle of prevention and surveillance of diseases. Tehran, Iran: Ministry of Health and Medical Education & RohGhalam 2005; 61-8.
5. Verma R, Khanna P, Mehta B. Revised national tuberculosis control program in India: the need to strengthen. Int J Prev Med 2013; 4(1): 1-5.
6. Tiemersma EW, van der Werf MJ, Borgdorff MW, Williams BG, Nagelkerke NJ. Natural history of tuberculosis: duration and fatality of untreated pulmonary tuberculosis in HIV negative patients: a systematic review. PloS One 2011; 6(4): e17601 .
7. World Health Organization. The End TB Strategy. Global strategy and targets for tuberculosis prevention, care and control after 2015. World Health Organization; 2014.
8. Maher D, Chaulet P, Spinaci S, Harries A. Treatment of tuberculosis: guidelines for national programmes. World Health Organization; 2003.
9. Stop TB Partnership & World Health Organization. The global plan to stop TB, 2006-2015 / Stop TB Partnership. World Health Organization; 2006.
10. Vassall A, Siapka M, Foster N, Cunnama L, Ramma L, Fielding K, et al. Cost-effectiveness of Xpert MTB/RIF for tuberculosis diagnosis in South Africa: a real-world cost analysis and economic evaluation. Lancet Glob Health 2017; 5(7): e710-e9.
11. Creswell J, Codlin AJ, Andre E, Micek MA, Bedru A, Carter EJ, et al. Results from early programmatic implementation of Xpert MTB/RIF testing in nine countries. BMC Infectious Diseases 2014; 14(1): 2.
12. Boyles T. Why do clinical trials of Xpert® MTB/RIF fail to show an effect on patient relevant outcomes? Int J Tuberc Lung Dis 2017; 21(3): 249-50.
13. van’t Hoog A, Langendam M, Mitchell E, Cobelens F, Sinclair D, Leeflang M. A systematic review of the sensitivity and specificity of symptom-and chest-radiography screening for active pulmonary tuberculosis in HIV-negative persons and persons with unknown HIV status. REPORT-Version March 2013. Geneva, Switzerland: WHO; 2013.
14. van’t Hoog AH, Meme HK, Laserson KF, Agaya JA, Muchiri BG, Githui WA, et al. Screening strategies for tuberculosis prevalence surveys: the value of chest radiography and symptoms. PloS One 2012; 7(7):e38691.
15. Getahun H, Kittikraisak W, Heilig CM, Corbett EL, Ayles H, Cain KP, et al. Development of a standardized screening rule for tuberculosis in people living with HIV in resource-constrained settings: individual participant data meta-analysis of observational studies. PLoS Med 2011; 8(1): e1000391.
16. van Cleeff MR A, Kivihya-Ndugga L, Githui W, Nganga L, Odhiambo J, Klatser P. A comprehensive study of the efficiency of the routine pulmonary tuberculosis diagnostic process in Nairobi. Int J Tuberc Lung Dis 2003; 7(2): 186-9.
17. Kranzer K, Afnan-Holmes H, Tomlin K, Golub JE, Shapiro A, Schaap A, et al. The benefits to communities and individuals of screening for active tuberculosis disease: a systematic review. Int J Tuberc Lung Dis 2013; 17(4): 432-46.
18. Jensen PA, Lambert LA, Iademarco MF, Ridzon R. Guidelines for preventing the transmission of Mycobacterium tuberculosis in health-care settings, 2005. Morbidity and Mortality Weekly Report (MMWR) 2005.
19. Mohammed A, Ehrlich R, Wood R, Cilliers F, Maartens G. Screening for tuberculosis in adults with advanced HIV infection prior to preventive therapy. Int J Tuberc Lung Dis 2004; 8(6): 792-5.
20. Wisnivesky JP, Serebrisky D, Moore C, Sacks HS, Iannuzzi MC, McGinn T. Validity of clinical prediction rules for isolating inpatients with suspected tuberculosis: a systematic review. J Gen Intern Med 2005; 20(10): 947-52.
21. Solari L, Acuna-Villaorduna C, Soto A, Agapito J, Perez F, Samalvides F, et al. A clinical prediction rule for pulmonary tuberculosis in emergency departments. Int J Tuberc Lung Dis 2008; 12(6): 619-24.
22. Were W, Moore D, Ekwaru P, Mwima G, Bunnell R, Kaharuza F, et al. A simple screening tool for active tuberculosis in HIV-infected adults receiving antiretroviral treatment in Uganda. Int J Tuberc Lung Dis 2009; 13(1): 47-53.
23. Cain KP, McCarthy KD, Heilig CM, Monkongdee P, Tasaneeyapan T, Kanara N, et al. An algorithm for tuberculosis screening and diagnosis in people with HIV. N Engl J Med 2010; 362: 707-16.
24. Swai HF, Mugusi FM, Mbwambo JK. Sputum smear negative pulmonary tuberculosis: sensitivity and specificity of diagnostic algorithm. BMC Res Notes 2011; 4(1): 475.
25. de Queiroz Mello FC, do Valle Bastos LG, Soares SLM, Rezende VM, Conde MB, Chaisson RE, et al. Predicting smear negative pulmonary tuberculosis with classification trees and logistic regression: a cross-sectional study. BMC Public Health 2006; 6(1): 43.
26. Aguiar FS, Almeida LL, Ruffino-Netto A, Kritski AL, Mello FC, Werneck GL. Classification and regression tree (CART) model to predict pulmonary tuberculosis in hospitalized patients. BMC Pulm Med 2012; 12(1): 40.
27. El-Solh AA, Hsiao C-B, Goodnough S, Serghani J, Grant BJ. Predicting active pulmonary tuberculosis using an artificial neural network. Chest 1999; 116(4): 968-73.
28. Er O, Temurtas F, Tanrıkulu AÇ. Tuberculosis disease diagnosis using artificial neural networks. J Med Syst 2010; 34(3): 299-302.
29. Elveren E, Yumuşak N. Tuberculosis disease diagnosis using artificial neural network trained with genetic algorithm. Journal of Medical Systems 2011; 35(3): 329-32.
30. Seixas J, Faria J, Souza F, Vieira A, Kritski A, Trajman A. Artificial neural network models to support the diagnosis of pleural tuberculosis in adult patients. Int J Tuberc Lung Dis 2013; 17(5): 682-6.
31. Breiman L. Random forests. Machine Learning 2001; 45(1): 5-32.
32. Brungard CW, Boettinger JL, Duniway MC, Wills SA, Edwards Jr TC. Machine learning for predicting soil classes in three semi-arid landscapes. Geoderma 2015; 239-240: 68-83.
33. Team R. RStudio: Integrated development environment for R. Boston, MA; 2016.
34. Kalhori S, Zeng X. Evaluation and comparison of different machine learning methods to predict outcome of tuberculosis treatment course. Journal of Intelligent Learning Systems and Applications 2013; 5(3): 184-93.
35. Asha T, Natarajan S, Murthy KNB. Effective classification algorithms to predict the accuracy of tuberculosis - A machine learning approach. Int J Com Science & Inf Security 2011; 9(7): 89-94.
36. Safari M, Sadeqifar M, Roshanaie GH, Zhiri A. Application of hidden markov model in forecasting new cases of tuberculosis in Hamadan province based on the recorded cases during 2006-2016. Iranian J Epid 2018; 14(2): 126-35.
37. Klotz A, Harouna A, Smith AF. Forecast analysis of the incidence of tuberculosis in the province of Quebec. BMC Public Health 2013; 13: 400.
38. Cao S, Wang F, Tam W, Tse LA, Kim JH, Liu J, et al. A hybrid seasonal prediction model for tuberculosis incidence in China. BMC Med Inform Decis Mak 2013; 13: 56-63.
39. Daliri Shamsabadi H, Ebrahimpour Koumleh H. Predicting tuberculosis using smart data mining algorithms. Proceedings of the National Conference on Computer Engineering and Information Technology, Shoshtar, Iran: 2013 Mar. [In Persian].