Use of artificial intelligence techniques for diagnosis of malignant pleural mesothelioma

Amac: Insanlarin beyin zarinda bulunan, asbestos ve asbestiform liflerine maruz kalmakla olusan kotu huylu plevral Mezotelyoma, oldukca saldirgan bir tumordur. Dusuk seviyeli cevresel erionite fibrous zeolite’e maruz birakilmis Turkiye’deki bazi kasabalarda Mezotelyoma gorulme orani oldukca yuksektir.Yontemler: Bu calismada Mezotelyoma hastaligi teshisi yapay bagisiklik sistemi kullanimi ile gerceklestirilmistir. Bununla beraber yapay bagisiklik sistemi sonuclari, ayni veri tabanini kullanan, Mezotelyoma hastaliginin teshisine odaklanmis cok katmanli yapay sinir agi sonuclari ile karsilastirilmistir. Mezotelyoma hastaligi veri seti, hastalarin hastane raporlarini kullanan tip fakultesi veri tabanindan alinmistir.Bulgular: Yapay bagisiklik sistemi tarafindan hastalik teshisi icin %97,74 dogruluk oraninda bir performans elde edilmistir. Yapay bagisiklik sistemi algoritmasinin dogruluk sonuclari cok katmanli yapay sinir agi algoritmasindan cok daha iyi oldugu gorulmustur.Sonuc: Bu sistem uzmana, saglikli ve hasta kisiyi siniflandirma surecinde dogru teshisi bulma yonunde iyi bir performans saglar. Boylece bu yapi ile dogru teshis sonucuna ulasmada doktorlara bir karar destek sistemi olarak yardimci olur

[1]  T. Yıldırım,et al.  MEDICAL DIAGNOSIS ON PIMA INDIAN DIABETES USING GENERAL REGRESSION NEURAL NETWORKS , 2003 .

[2]  Cengiz Sertkaya,et al.  A Comparative Study on Chronic Obstructive Pulmonary and Pneumonia Diseases Diagnosis using Neural Networks and Artificial Immune System , 2009, Journal of Medical Systems.

[3]  F. Ricceri,et al.  Survival of pleural malignant mesothelioma in Italy: A population‐based study , 2009, International journal of cancer.

[4]  C. A. van der Merwe,et al.  Malignant pleural mesothelioma: a disease unaffected by current therapeutic maneuvers. , 1988, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[5]  D. Dasgupta,et al.  Advances in artificial immune systems , 2006, IEEE Computational Intelligence Magazine.

[6]  Fevzullah Temurtas,et al.  A comparative study on thyroid disease diagnosis using neural networks , 2009, Expert Syst. Appl..

[7]  Krzysztof Trojanowski,et al.  Searching for Memory in Artificial Immune System , 2002, Intelligent Information Systems.

[8]  Mohammad Bagher Menhaj,et al.  Training feedforward networks with the Marquardt algorithm , 1994, IEEE Trans. Neural Networks.

[9]  S. Emri,et al.  Prognostic significance of flow cytometric DNA analysis in patients with malignant pleural mesothelioma. , 2001, Lung cancer.

[10]  Leandro Nunes de Castro,et al.  The Clonal Selection Algorithm with Engineering Applications 1 , 2000 .

[11]  Alberto Tesi,et al.  On the Problem of Local Minima in Backpropagation , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[13]  Fevzullah Temurtas,et al.  Tuberculosis Disease Diagnosis Using Artificial Neural Networks , 2010, Journal of Medical Systems.

[14]  S. Emri,et al.  Assessment of environmental asbestos exposure in Turkey by bronchoalveolar lavage. , 1998, American journal of respiratory and critical care medicine.

[15]  Orhan Engin,et al.  ARTIFICIAL IMMUNE SYSTEMS AND APPLICATIONS IN INDUSTRIAL PROBLEMS , 2004 .

[16]  H. Pass,et al.  Malignant mesothelioma 2008 , 2008, Current opinion in pulmonary medicine.

[17]  Fevzullah Temurtas,et al.  An approach based on probabilistic neural network for diagnosis of Mesothelioma's disease , 2012, Comput. Electr. Eng..

[18]  A. B. Watkins,et al.  A resource limited artificial immune classifier , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[19]  Dursun Delen,et al.  Predicting breast cancer survivability: a comparison of three data mining methods , 2005, Artif. Intell. Medicine.

[20]  I. Uçgun,et al.  Prognostic factors in diffuse malignant pleural mesothelioma: effects of pretreatment clinical and laboratory characteristics. , 2001, Respiratory medicine.

[21]  D. Sugarbaker,et al.  Mesothelioma and radical multimodality therapy: who benefits? , 1995, Chest.

[22]  Martin T. Hagan,et al.  Neural network design , 1995 .

[23]  Fevzullah Temurtas,et al.  A study on quantitative classification of binary gas mixture using neural networks and adaptive neuro-fuzzy inference systems , 2006 .

[24]  H. Bayram,et al.  Malignant Pleural Mesothelioma Caused by Environmental Exposure to Asbestos in the Southeast of Turkey: CT Findings in 117 Patients , 2000, Respiration.

[25]  K R Abrams,et al.  Prognostic factors for malignant mesothelioma in 142 patients: validation of CALGB and EORTC prognostic scoring systems , 2000, Thorax.

[26]  G. Dabouis,et al.  Prognostic factors in diffuse malignant pleural mesothelioma. A study of 167 patients. , 1988, Chest.

[27]  Tulay Yildirim,et al.  Diagnosis of thyroid disease using artificial neural network methods , 2002, Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02..

[28]  A. Hart,et al.  Prognostic value of the serum tumour markers Cyfra 21-1 and tissue polypeptide antigen in malignant mesothelioma. , 1999, Lung cancer.

[29]  F Levi,et al.  The European mesothelioma epidemic , 1999, British Journal of Cancer.

[30]  R. Damhuis,et al.  Prognostic factors in malignant mesothelioma. , 2004, Lung cancer.

[31]  D. Sugarbaker,et al.  Multimodality management of malignant pleural mesothelioma. , 1998, Chest.

[32]  A. Demir,et al.  Environmental fibrous zeolite (erionite) exposure and malignant tumors other than mesothelioma. , 1996, Journal of environmental pathology, toxicology and oncology : official organ of the International Society for Environmental Toxicology and Cancer.

[33]  Halife Kodaz,et al.  Medical application of information gain based artificial immune recognition system (AIRS): Diagnosis of thyroid disease , 2009, Expert Syst. Appl..

[34]  Abdurrahman Abakay,et al.  Environmental malignant pleural mesothelioma in Southeast Turkey. , 2006, Saudi medical journal.

[35]  S. Emri,et al.  Malignant pleural mesothelioma due to environmental mineral fiber exposure in Turkey. Analysis of 135 cases. , 1992, Chest.

[36]  N. Vogelzang.  Malignant mesothelioma: diagnostic and management strategies for 1992. , 1992, Seminars in oncology.

[37]  Richard P. Brent,et al.  Fast training algorithms for multilayer neural nets , 1991, IEEE Trans. Neural Networks.

[38]  A. Şenyiğit,et al.  Incidence of Malignant Pleural Mesothelioma due to Environmental Asbestos Fiber Exposure in the Southeast of Turkey , 2000, Respiration.

[39]  Fevzullah Temurtas,et al.  A Study on Chronic Obstructive Pulmonary Disease Diagnosis Using Multilayer Neural Networks , 2008, Journal of Medical Systems.

[40]  Nuray Can,et al.  Warthin benzeri papiller tiroid karsinomu: Nadir bir tiroid tümörü , 2011 .

[41]  Fevzullah Temurtas,et al.  Chest diseases diagnosis using artificial neural networks , 2010, Expert Syst. Appl..