Yıl 2015,
Cilt: 42 Sayı: 1, 5 - 11, 09.05.2015
Öz
Objective: Malignant pleural mesothelioma is a highly aggressive tumor of the serous membranes, which in humans results from exposure to asbestos and asbestiform fibers. The incidence of malignant mesothelioma is extremely high in some Turkish villages where there is a low-level environmental exposure to erionite, a fibrous zeolite. Therefore epidemiological studies are difficult to perform in Turkey. Methods: In this paper, a study on malignant pleural mesothelioma disease diagnosis was realized by using artificial immune system. Also, the artificial immune system result was compared with the result of the multi-layer neural network focusing on malignant pleural mesothelioma disease diagnosis and using same database. The malignant pleural mesothelioma disease dataset were prepared from a faculty of medicine’s database using patient’s hospital reports. Results: 97.74% accuracy performance is obtained by artificial immune system. The accuracy results of artificial immune system algorithm are much better than the accuracy results of multi-layer neural network algorithm. Conclusion: This system is capable of conducting the classification process with a good performance to help the expert while deciding the healthy and patient subjects. So, this structure can be helpful as learning based decision support system for contributing to the doctors in their diagnosis decisions. Key words: malignant pleural mesothelioma disease diagnosis, artificial immune system, machine learning based decision support system.
Anahtar Kelimeler
malignant pleural mesothelioma disease diagnosis artificial immune system machine learning based decision support system
Kaynakça
- Wagner JC, Sleggs CA, Marchand P. Diffuse pleural mesothelioma
- and asbestos exposure in the North Western Cape
- Povince. Br J Indust Med 1960;17:266-271.
- Emri S, Akbulut H, Zorlu F, et al. Prognostic significance of
- flow cytometric DNA analysis in patients with malignant
- pleural mesothelioma. Lung Cancer 2001; 33:109-114.
- Barış B, Demir AU, Shehu V, et al. Environmental fibrous
- zeolite (erionite) exposure and malignant tumors other
- than mesothelioma. J Environ Pathol Toxicol Oncol 1996;
- :183-189.
- Dumortier P, Çöplü L, De Maertelaer V, et al. Assessment of
- environmental asbestos exposure in Turkey by bronchoalveolar
- lavage. Am J Respir Crit Care Med 1998; 158:1815-
- -
- Selcuk ZT, Coplu L, Emri S, et al. Malignant pleural mesothelioma
- due to environmental mineral fiber exposure in
- Turkey. Analysis of 135 cases. Chest 1992;102:790-796.
- Peto J, Decarli A, La Vecchia C, et al. The European mesothelioma
- epidemic Brit J Cancer 1999;79:666-672.
- Burgers JA, Damhuis RAM, Prognostic factors in malignant
- mesothelioma Lung Cancer 2004;56:123-129.
- Zervos MD, Bizekis C. Pass HI: Malignant mesothelioma
- Curr Opin Pulm Med 2008, 14:303-309.
- National Mesothelioma committee, http://www.mesothelioma-tr.org/
- (last accepted: 10 November 2014)
- Tanrikulu AC, Senyigit A, Dagli CE, et al. Environmental
- malignant pleural mesothelioma in Southeast Turkey. Saudi
- Med J 2006;27:1605-1607.
- Senyiğit A, Bayram H, Babayiğit C, et al. Malignant pleural
- mesothelioma caused by environmental exposure to asbestos
- in the Southeast of Turkey: CT findings in 117 patients.
- Respiration 2000;67:615-622.
- Senyiğit A, Babayiğit C, Gökirmak M, et al. Incidence of
- malignant pleural mesothelioma due to environmental asbestos
- fiber exposure in the southeast of Turkey. Respiration
- ;67:610-614.
- Can N, Puyan FÖ, Öz F, et al. Warthin-like papillary thyroid
- carcinoma: A rare tumor of the thyroid. Dicle Medical Journal
- ;38 :482-485.
- Alberts AS, Faikson G, Goedhals L, et al. Malignant pleural
- mesothelioma: a disease unaffected by current therapeutic
- maneuvers. J. Clin Oncol 1988;6:527-535.
- Vogelzang NJ. Malignant mesothelioma: diagnostic and
- management strategies for 1992. Semin Oncol 1992;19:64-
- -
- Chailleux E, Dabouis G, Pioche D, et al. Prognostic factors
- in diffuse malignant pleural mesothelioma. Chest
- ;93:159-162.
- Schouwink H, Korse CM, Bonfrer JM, et al. Prognostic
- value of the serum tumour markers Cyfra 21-1 and tissue
- polypeptide antigen in malignant mesothelioma. Lung Cancer
- ;25:25-32.
- Sugarbaker D.J., Jaklitseh M.T., Liptay M.J., Mesothelioma
- and radical multimodality therapy: who benefits? Chest
- ; 107(Suppl.): 3455-3505.
- Sugarbaker D.J., Norberto J.J., Multimodality management
- of malignant pleural mesothelioma. Chest 1998; 113 (Suppl.):
- -655.
- Metintas M, Metintas S, Ucgun I, et al. Prognostic factors in
- diffuse malignant pleural mesothelioma: pretreatment clinical
- and laboratory characteristics. Resp. Medicine 2001:
- :829-835.
- O. Er, et al. Artificial intelligence techniques for diagnosis of malignant pleural mesothelioma 11
- Dicle Tıp Derg / Dicle Med J www.diclemedj.org Cilt / Vol 42, No 1, 5-11
- Edwards JG, Abrams KR, Leverment JN. Prognostic factors
- for malignant mesothelioma in 142 patients: validation of
- CALGB and EORTC prognostic scoring systems. Thorax
- ;55:731-735.
- Curran D, Sahmoud T, Therasse P. Prognostic factors
- in patients with pleural mesothelioma: the European
- Organisa¬tion for research and treatment of cancer experience.
- J Clin OncoI 1998;16:145-152.
- Montanaro F, Rosato R, Gangemi M, et al. Survival of pleural
- malignant mesothelioma in Italy: a population-based
- study. Int J Cancer. 2009;124:194-200.
- Kadoz H, Ozsen S, Arslan A and Gunes S. Medical application
- of information gain based artificial immune recognition
- system (AIRS): Diagnosis of thyroid disease. Expert
- System with Application. 2008;36:3086-3092.
- Engin O, Döyen A. Artificial Immune Systems and Applications
- in Industrial Problems. GU J Sci 2004;17:71-84.
- Dasgupta D. Advances in Artificial Immune Systems. EEE
- Computational Intelligence Magazine, University of Memphis,
- USA, November 2006; 40-49.
- Kayaer K, Yıldırım T. Medical Diagnosis on Pima Indian
- Diabetes Using General Regression Neural Networks. In
- Proc. of International Conference on Artificial Neural Networks
- and Neural Information Processing (ICANN/ICONIP),
- Istanbul, 2003; 181-184.
- Delen D, Walker G, Kadam A., Predicting breast cancer
- survivability: A comparison of three data mining methods,
- Artificial Intelligence in Medicine Artificial Intelligence in
- Medicine, 2005; 34: 113–127.
- Temurtas F. A comparative study on thyroid disease diagnosis
- using neural networks. Expert Systems with Applications,
- ; 36: 944-949.
- Rumelhart DE, Hinton GE, Williams RJ. Learning internal
- representations by error propagation, in D.E. Rumelhart,
- J.L. McClelland (Eds.), Parallel Distributed Processing:
- Explorations in the Microstructure of Cognition, 1986; 1:
- –362.
- Brent RP. Fast training algorithms for multi-layer neural
- nets. IEEE Trans. Neural Networks, 1991; 2: 346–354.
- Gori M, Tesi A. On the problem of local minima in backpropagation.
- IEEE Trans. Pattern Anal. Machine Intell.
- ; 14: 76–85.
- Hagan MT, Menhaj M. Training feed forward networks
- with the Marquardt algorithm. IEEE Trans. Neural Networks,
- ; 5: 989-993.
- Hagan MT, Demuth HB, Beale MH. Neural Network Design.
- PWS Publishing, Boston, MA, 1996.
- Gulbag A, Temurtas F. A study on quantitative classification
- of binary gas mixture using neural networks and adaptive
- neuro fuzzy inference systems. Sensor Actuators B, 2006;
- : 252-262.
- Trojanowski K, Wierzchon ST. Searching for Memory in
- Artificial Immune System. The Eleventh International
- Symposium on Intelligent Information Systems, June 3-6,
- -
- De Castro LN and Timmis J. A Novel approach to pattern
- recognition. Artificial Neural Networks in pattern Recognition,
- University of Paisley: 2002; 67-84.
- De Castro LN and Von Zuben FJ. The clonal selection algorithm
- with engineering applications. GECCO 2000, Las
- Vegas, Nevada, USA, July 8, 2000.
- Er O, Sertkaya C, Temurtas F and Tanrikulu AC. A Comparative
- study on chronic obstructive pulmonary and pneumonia
- diseases diagnosis using neural networks and artificial
- immune system. Journal of Medical Systems, 2009;
- : 485-492.
- Watkins A. AIRS: A resource limited artificial immune classifier.
- Master Thesis, Mississippi State University, 2001.
- Sertkaya C. Immune Base System in Computer Security,
- Master Thesis. Sakarya University. Institute of Science and
- Technology, 2009.
- Er O, Yumusak N, Temurtas F. Chest diseases diagnosis using
- artificial neural networks. Expert Systems with Applications,
- ; 37: 7648-7655.
- Ozyılmaz L, Yıldırım T. Diagnosis of thyroid disease using
- artificial neural network methods. In Proc. of ICONIP’02
- th international conference on neural information processing,
- Orchid Country Club, Singapore, 2002; 2033–2036.
- Er O, Temurtas F. A study on chronic obstructive pulmonary
- disease diagnosis using multilayer neural networks. Journal
- of Medical Systems, 2008; 32: 429-432.
- Er O, Temurtas F, Tanrikulu AC. Tuberculosis disease diagnosis
- using artificial neural networks. J Med Systems
- ;34:299-302.
- Er O, Tanrikulu AC, Abakay A, Temurtas F. An approach
- based on probabilistic neural network for diagnosis of Mesothelioma’s
- disease. Computers & Electrical Engineering
- ;38:75-81.
Use of artificial intelligence techniques for diagnosis of malignant pleural mesothelioma Malign plevral mezotelyoma tanısı için yapay zeka teknikleri kullanımı
Öz
Amaç: İnsanların beyin zarında bulunan, asbestos ve asbestiform liflerine maruz kalmakla oluşan kötü huylu plevral Mezotelyoma, oldukça saldırgan bir tümördür. Düşük seviyeli çevresel erionite fibrous zeolite’e maruz bırakılmış Türkiye’deki bazı kasabalarda Mezotelyoma görülme oranı oldukça yüksektir.Yöntemler: Bu çalışmada Mezotelyoma hastalığı teşhisi yapay bağışıklık sistemi kullanımı ile gerçekleştirilmiştir. Bununla beraber yapay bağışıklık sistemi sonuçları, aynı veri tabanını kullanan, Mezotelyoma hastalığının teşhisine odaklanmış çok katmanlı yapay sinir ağı sonuçları ile karşılaştırılmıştır. Mezotelyoma hastalığı veri seti, hastaların hastane raporlarını kullanan tıp fakültesi veri tabanından alınmıştır.Bulgular: Yapay bağışıklık sistemi tarafından hastalık teşhisi için %97,74 doğruluk oranında bir performans elde edilmiştir. Yapay bağışıklık sistemi algoritmasının doğruluk sonuçları çok katmanlı yapay sinir ağı algoritmasından çok daha iyi olduğu görülmüştür.Sonuç: Bu sistem uzmana, sağlıklı ve hasta kişiyi sınıflandırma sürecinde doğru teşhisi bulma yönünde iyi bir performans sağlar. Böylece bu yapı ile doğru teşhis sonucuna ulaşmada doktorlara bir karar destek sistemi olarak yardımcı olur
Anahtar Kelimeler
Kötü huylu plevral mezotelyoma hastalığının teşhisi yapay bağışıklık sistemi makine öğrenme tabanlı karar destek sistemi
Kaynakça
- Wagner JC, Sleggs CA, Marchand P. Diffuse pleural mesothelioma
- and asbestos exposure in the North Western Cape
- Povince. Br J Indust Med 1960;17:266-271.
- Emri S, Akbulut H, Zorlu F, et al. Prognostic significance of
- flow cytometric DNA analysis in patients with malignant
- pleural mesothelioma. Lung Cancer 2001; 33:109-114.
- Barış B, Demir AU, Shehu V, et al. Environmental fibrous
- zeolite (erionite) exposure and malignant tumors other
- than mesothelioma. J Environ Pathol Toxicol Oncol 1996;
- :183-189.
- Dumortier P, Çöplü L, De Maertelaer V, et al. Assessment of
- environmental asbestos exposure in Turkey by bronchoalveolar
- lavage. Am J Respir Crit Care Med 1998; 158:1815-
- -
- Selcuk ZT, Coplu L, Emri S, et al. Malignant pleural mesothelioma
- due to environmental mineral fiber exposure in
- Turkey. Analysis of 135 cases. Chest 1992;102:790-796.
- Peto J, Decarli A, La Vecchia C, et al. The European mesothelioma
- epidemic Brit J Cancer 1999;79:666-672.
- Burgers JA, Damhuis RAM, Prognostic factors in malignant
- mesothelioma Lung Cancer 2004;56:123-129.
- Zervos MD, Bizekis C. Pass HI: Malignant mesothelioma
- Curr Opin Pulm Med 2008, 14:303-309.
- National Mesothelioma committee, http://www.mesothelioma-tr.org/
- (last accepted: 10 November 2014)
- Tanrikulu AC, Senyigit A, Dagli CE, et al. Environmental
- malignant pleural mesothelioma in Southeast Turkey. Saudi
- Med J 2006;27:1605-1607.
- Senyiğit A, Bayram H, Babayiğit C, et al. Malignant pleural
- mesothelioma caused by environmental exposure to asbestos
- in the Southeast of Turkey: CT findings in 117 patients.
- Respiration 2000;67:615-622.
- Senyiğit A, Babayiğit C, Gökirmak M, et al. Incidence of
- malignant pleural mesothelioma due to environmental asbestos
- fiber exposure in the southeast of Turkey. Respiration
- ;67:610-614.
- Can N, Puyan FÖ, Öz F, et al. Warthin-like papillary thyroid
- carcinoma: A rare tumor of the thyroid. Dicle Medical Journal
- ;38 :482-485.
- Alberts AS, Faikson G, Goedhals L, et al. Malignant pleural
- mesothelioma: a disease unaffected by current therapeutic
- maneuvers. J. Clin Oncol 1988;6:527-535.
- Vogelzang NJ. Malignant mesothelioma: diagnostic and
- management strategies for 1992. Semin Oncol 1992;19:64-
- -
- Chailleux E, Dabouis G, Pioche D, et al. Prognostic factors
- in diffuse malignant pleural mesothelioma. Chest
- ;93:159-162.
- Schouwink H, Korse CM, Bonfrer JM, et al. Prognostic
- value of the serum tumour markers Cyfra 21-1 and tissue
- polypeptide antigen in malignant mesothelioma. Lung Cancer
- ;25:25-32.
- Sugarbaker D.J., Jaklitseh M.T., Liptay M.J., Mesothelioma
- and radical multimodality therapy: who benefits? Chest
- ; 107(Suppl.): 3455-3505.
- Sugarbaker D.J., Norberto J.J., Multimodality management
- of malignant pleural mesothelioma. Chest 1998; 113 (Suppl.):
- -655.
- Metintas M, Metintas S, Ucgun I, et al. Prognostic factors in
- diffuse malignant pleural mesothelioma: pretreatment clinical
- and laboratory characteristics. Resp. Medicine 2001:
- :829-835.
- O. Er, et al. Artificial intelligence techniques for diagnosis of malignant pleural mesothelioma 11
- Dicle Tıp Derg / Dicle Med J www.diclemedj.org Cilt / Vol 42, No 1, 5-11
- Edwards JG, Abrams KR, Leverment JN. Prognostic factors
- for malignant mesothelioma in 142 patients: validation of
- CALGB and EORTC prognostic scoring systems. Thorax
- ;55:731-735.
- Curran D, Sahmoud T, Therasse P. Prognostic factors
- in patients with pleural mesothelioma: the European
- Organisa¬tion for research and treatment of cancer experience.
- J Clin OncoI 1998;16:145-152.
- Montanaro F, Rosato R, Gangemi M, et al. Survival of pleural
- malignant mesothelioma in Italy: a population-based
- study. Int J Cancer. 2009;124:194-200.
- Kadoz H, Ozsen S, Arslan A and Gunes S. Medical application
- of information gain based artificial immune recognition
- system (AIRS): Diagnosis of thyroid disease. Expert
- System with Application. 2008;36:3086-3092.
- Engin O, Döyen A. Artificial Immune Systems and Applications
- in Industrial Problems. GU J Sci 2004;17:71-84.
- Dasgupta D. Advances in Artificial Immune Systems. EEE
- Computational Intelligence Magazine, University of Memphis,
- USA, November 2006; 40-49.
- Kayaer K, Yıldırım T. Medical Diagnosis on Pima Indian
- Diabetes Using General Regression Neural Networks. In
- Proc. of International Conference on Artificial Neural Networks
- and Neural Information Processing (ICANN/ICONIP),
- Istanbul, 2003; 181-184.
- Delen D, Walker G, Kadam A., Predicting breast cancer
- survivability: A comparison of three data mining methods,
- Artificial Intelligence in Medicine Artificial Intelligence in
- Medicine, 2005; 34: 113–127.
- Temurtas F. A comparative study on thyroid disease diagnosis
- using neural networks. Expert Systems with Applications,
- ; 36: 944-949.
- Rumelhart DE, Hinton GE, Williams RJ. Learning internal
- representations by error propagation, in D.E. Rumelhart,
- J.L. McClelland (Eds.), Parallel Distributed Processing:
- Explorations in the Microstructure of Cognition, 1986; 1:
- –362.
- Brent RP. Fast training algorithms for multi-layer neural
- nets. IEEE Trans. Neural Networks, 1991; 2: 346–354.
- Gori M, Tesi A. On the problem of local minima in backpropagation.
- IEEE Trans. Pattern Anal. Machine Intell.
- ; 14: 76–85.
- Hagan MT, Menhaj M. Training feed forward networks
- with the Marquardt algorithm. IEEE Trans. Neural Networks,
- ; 5: 989-993.
- Hagan MT, Demuth HB, Beale MH. Neural Network Design.
- PWS Publishing, Boston, MA, 1996.
- Gulbag A, Temurtas F. A study on quantitative classification
- of binary gas mixture using neural networks and adaptive
- neuro fuzzy inference systems. Sensor Actuators B, 2006;
- : 252-262.
- Trojanowski K, Wierzchon ST. Searching for Memory in
- Artificial Immune System. The Eleventh International
- Symposium on Intelligent Information Systems, June 3-6,
- -
- De Castro LN and Timmis J. A Novel approach to pattern
- recognition. Artificial Neural Networks in pattern Recognition,
- University of Paisley: 2002; 67-84.
- De Castro LN and Von Zuben FJ. The clonal selection algorithm
- with engineering applications. GECCO 2000, Las
- Vegas, Nevada, USA, July 8, 2000.
- Er O, Sertkaya C, Temurtas F and Tanrikulu AC. A Comparative
- study on chronic obstructive pulmonary and pneumonia
- diseases diagnosis using neural networks and artificial
- immune system. Journal of Medical Systems, 2009;
- : 485-492.
- Watkins A. AIRS: A resource limited artificial immune classifier.
- Master Thesis, Mississippi State University, 2001.
- Sertkaya C. Immune Base System in Computer Security,
- Master Thesis. Sakarya University. Institute of Science and
- Technology, 2009.
- Er O, Yumusak N, Temurtas F. Chest diseases diagnosis using
- artificial neural networks. Expert Systems with Applications,
- ; 37: 7648-7655.
- Ozyılmaz L, Yıldırım T. Diagnosis of thyroid disease using
- artificial neural network methods. In Proc. of ICONIP’02
- th international conference on neural information processing,
- Orchid Country Club, Singapore, 2002; 2033–2036.
- Er O, Temurtas F. A study on chronic obstructive pulmonary
- disease diagnosis using multilayer neural networks. Journal
- of Medical Systems, 2008; 32: 429-432.
- Er O, Temurtas F, Tanrikulu AC. Tuberculosis disease diagnosis
- using artificial neural networks. J Med Systems
- ;34:299-302.
- Er O, Tanrikulu AC, Abakay A, Temurtas F. An approach
- based on probabilistic neural network for diagnosis of Mesothelioma’s
- disease. Computers & Electrical Engineering
- ;38:75-81.
Toplam 152 adet kaynakça vardır.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Bölüm | Araştırma Yazıları |
| Yazarlar | |
| Yayımlanma Tarihi | 9 Mayıs 2015 |
| Gönderilme Tarihi | 9 Mayıs 2015 |
| Yayımlandığı Sayı | Yıl 2015 Cilt: 42 Sayı: 1 |