2,706
Views
6
CrossRef citations to date
0
Altmetric
COMPUTER SCIENCE

A novel fuzzy expert system design to assist with peptic ulcer disease diagnosis

ORCID Icon, ORCID Icon & ORCID Icon | (Reviewing editor)
Article: 1861730 | Received 01 Oct 2019, Accepted 05 Dec 2020, Published online: 07 Jan 2021

References

  • Abbass, H. A. (2002). An evolutionary artificial neural networks approach for breast cancer diagnosis. Artificial Intelligence in Medicine, 25(3), 265–23. https://doi.org/10.1016/S0933-3657(02)00028-3
  • Abraham, A. (2005). Rule‐based expert systems (Handbook of measuring system design), Wiley Online Library
  • Adeli, A., & Neshat, M. (2010). A fuzzy expert system for heart disease diagnosis. In Proceedings of international multi conference of engineers and computer scientists. Hong Kong.
  • Aghaei, F., Ross, S. R., Wang, Y., Wu, D. H., Cornwell, B. O., Ray, B., & Zheng, B. (2017, March). Implementation of a computer-aided detection tool for quantification of intracranial radiologic markers on brain CT images. In Medical Imaging 2017: Imaging Informatics for Healthcare, Research, and Applications (Vol. 10138, p. 1013805). International Society for Optics and Photonics
  • Bakhsheshi, M. F., Ho, M., Keenliside, L., & Lee, T. Y. (2019). Non-invasive monitoring of brain temperature during rapid selective brain cooling by zero-heat-flux thermometry. Emerging Science Journal, 3(1), 1–9. https://doi.org/10.28991/esj-2019-01163
  • Bezdek, J. C., Ehrlich, R., & Full, W. (1984). FCM: The fuzzy c-means clustering algorithm. Computers & Geosciences, 10(2–3), 191–203. https://doi.org/10.1016/0098-3004(84)90020-7
  • Castanho, M. J., Barros, L. C., Yamakami, A., & Vendite, L. L. (2007). Fuzzy receiver operating characteristic curve: An option to evaluate diagnostic tests. IEEE Transactions on Information Technology in Biomedicine, 11(3), 244–250. https://doi.org/10.1109/TITB.2006.879593
  • Chadli, M., & Borne, P. (2013). Multiple models approach in automation. Takagi-Sugeno Fuzzy Systems, Wiley Online Library.
  • Charisis, V. S., Katsimerou, C., Hadjileontiadis, L. J., Liatsos, C. N., & Sergiadis, G. D. (2013, June). Computer-aided capsule endoscopy images evaluation based on color rotation and texture features: An educational tool to physicians. In Proceedings of the 26th IEEE International Symposium On Computer-Based Medical Systems (pp. 203–208). IEEE.
  • De Silva, C. W. (2018). Intelligent control: Fuzzy logic applications. CRC press.
  • Doi, K. (2007). Computer-aided diagnosis in medical imaging: Historical review, current status and future potential. Computerized Medical Imaging and Graphics, 31(4–5), 198–211. https://doi.org/10.1016/j.compmedimag.2007.02.002
  • Donabedian, A. (2005). Evaluating the quality of medical care. The Milbank Quarterly, 83(4), 691–729. https://doi.org/10.1111/j.1468-0009.2005.00397.x
  • Farahani, F. V., Zarandi, M. F., & Ahmadi, A. (2015). Fuzzy rule based expert system for diagnosis of lung cancer. In 2015 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS) held jointly with 2015 5th World Conference on Soft Computing (WConSC). IEEE.
  • Fashner, J., & Gitu, A. C. (2015). Diagnosis and treatment of peptic ulcer disease and H. pylori infection. American Family Physician, 91(4), 236–242. https://www.aafp.org/afp/2015/0215/p236.html
  • Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861–874. https://doi.org/10.1016/j.patrec.2005.10.010
  • Fried, L. P., Storer, D. J., King, D. E., & Lodder, F. (1991). Diagnosis of illness presentation in the elderly. Journal of the American Geriatrics Society, 39(2), 117–123. https://doi.org/10.1111/j.1532-5415.1991.tb01612.x
  • Giger, M., & MacMahon, H. (1996). Image processing and computer-aided diagnosis. Radiologic Clinics of North America, 34(3), 565–596. https://europepmc.org/article/med/8657872
  • Güvenir, H. A. (2003). Benefit maximization in classification on feature projections. In Proceedings of the 3rd IASTED International Conference on Artificial Intelligence and Applications, Malaga, Spain, 424–429
  • Güvenir, H. A., Emeksiz, N., Ikizler, N., & Örmeci, N. (2004). Diagnosis of gastric carcinoma by classification on feature projections. Artificial Intelligence in Medicine, 31(3), 231–240. https://doi.org/10.1016/j.artmed.2004.03.003
  • Hamidian, D., Salajegheh, J., & Salajegheh, E. (2018). Damage detection of irregular plates and regular dams by wavelet transform combined adoptive neuro fuzzy inference system. Civil Engineering Journal, 4(2), 305–319. https://doi.org/10.28991/cej-030993
  • Hassan, N., Sayed, O. R., Khalil, A. M., & Ghany, M. A. (2017). Fuzzy soft expert system in prediction of coronary artery disease. International Journal of Fuzzy Systems, 19(5), 1546–1559. https://doi.org/10.1007/s40815-016-0255-0
  • Hwang, J. J., Lee, D. H., Lee, A.-R., Yoon, H., Shin, C. M., Park, Y. S., & Kim, N. (2015). Characteristics of gastric cancer in peptic ulcer patients with Helicobacter pylori infection. World Journal of Gastroenterology: WJG, 21(16), 4954. https://doi.org/10.3748/wjg.v21.i16.4954
  • Jain, V., & Raheja, S. (2015). Improving the prediction rate of diabetes using Fuzzy Expert System. International Journal of Information Technology and Computer Science, 10(10), 84–91. https://doi.org/10.5815/ijitcs.2015.10.10
  • Jang, J. S. (1993). ANFIS: Adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics, 23(3), 665–685. https://doi.org/10.1109/21.256541
  • Kandel, A. (1991). Fuzzy expert systems. CRC press.
  • Kannan, R., & Vasanthi, V. (2019). Machine learning algorithms with ROC curve for predicting and diagnosing the heart disease. Soft Computing and Medical Bioinformatics. 63–72. Springer, Singapore. http://doi-org-443.webvpn.fjmu.edu.cn/10.1007/978-981-13-0059-2_8
  • Lai, S. T., Fung, K. P., Ng, F. H., & Lee, K. C. (1996). A quantitative analysis of symptoms of non-ulcer dyspepsia as related to age, pathology, and Helicobacter infection. Scandinavian Journal of Gastroenterology, 31(11), 1078–1082. https://doi.org/10.3109/00365529609036890
  • Laine, L., & Peterson, W. L. (1994). Bleeding peptic ulcer. New England Journal of Medicine, 331(11), 717–727. https://doi.org/10.1056/NEJM199409153311107
  • Lanas, A., & Chan, F. K. (2017). Peptic ulcer disease. The Lancet, 390(10094), 613–624. https://doi.org/10.1016/S0140-6736(16)32404-7
  • Liedlgruber, M., & Uhl, A. (2011). A summary of research targeted at computer-aided decision support in endoscopy of the gastrointestinal tract. Department of Computer Sciences, University of Salzburg. Tech. Rep, 1. http://www.cosy.sbg.ac.at/research/tr.html
  • Maamri, F., Bououden, S., Chadli, M., & Boulkaibet, I. (2018). The Pachycondyla Apicalis metaheuristic algorithm for parameters identification of chaotic electrical system. International Journal of Parallel, Emergent and Distributed Systems, 33(5), 490–502. https://doi.org/10.1080/17445760.2017.1401622
  • Malfertheiner, P., Megraud, F., O’morain, C., Gisbert, J., Kuipers, E., Axon, A., Bazzoli, F., Gasbarrini, A., Atherton, J., & Graham, D. Y. (2017). Management of Helicobacter pylori infection—the Maastricht V/Florence consensus report. Gut, 66(1), 6–30.
  • Matthijs, G., Souche, E., Alders, M., Corveleyn, A., Eck, S., Feenstra, I., Race, V., Sistermans, E., Sturm, M., & Weiss, M. (2016). Guidelines for diagnostic next-generation sequencing. European Journal of Human Genetics, 24(1), 2. https://doi.org/10.1038/ejhg.2015.226
  • Moghaddam, G., Sharifzadeh, M., Hassanzadeh, G., Khanavi, M., & Hajimahmoodi, M. (2013). Anti-ulcerogenic activity of the pomegranate peel (Punica granatum) methanol extract. Food and Nutrition Sciences, 4(10), 43. https://doi.org/10.4236/fns.2013.410A008
  • Morice, A. H., Millqvist, E., Belvisi, M. G., Bieksiene, K., Birring, S. S., Chung, K. F., Dal Negro, R. W., Dicpinigaitis, P., Kantar, A., & McGarvey, L. P. (2014). Expert opinion on the cough hypersensitivity syndrome in respiratory medicine. European Respiratory Journal, 44(5), 1132–1148. https://doi.org/10.1183/09031936.00218613
  • Movahedi, M., Afsharfard, A., Moradi, A., Nasermoaddeli, A., Khoshnevis, J., Fattahi, F., & Akbari, M. E. (2009). Survival rate of gastric cancer in Iran. Journal of Research in Medical Sciences: The Official Journal of Isfahan University of Medical Sciences, 14(6), 367. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3129080/
  • Neshat, M., Yaghobi, M., Naghibi, M., & Esmaelzadeh, A. (2008). Fuzzy Expert System design for diagnosis of liver disorders. In 2008 international symposium on knowledge acquisition and modeling, Wuhan, 2008, pp. 252–256, IEEE
  • Pena-Reyes, C. A., & Sipper, M. (1999). A fuzzy-genetic approach to breast cancer diagnosis. Artificial Intelligence in Medicine, 17(2), 131–155. https://doi.org/10.1016/S0933-3657(99)00019-6
  • Pereira, C. R., Pereira, D. R., Weber, S. A., Hook, C., de Albuquerque, V. H. C., & Papa, J. P. (2019). A survey on computer-assisted Parkinson's disease diagnosis. Artificial intelligence in medicine, 95, 48–63
  • Peskar, B. M., Ehrlich, K., & Peskar, B. A. (2002). Role of ATP-sensitive potassium channels in prostaglandin-mediated gastroprotection in the rat. Journal of Pharmacology and Experimental Therapeutics, 301(3), 969–974. https://doi.org/10.1124/jpet.301.3.969
  • Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1(1), 81–106. https://doi.org/10.1007/BF00116251
  • Ramakrishnan, K., & Salinas, R. C. (2007). Peptic ulcer disease. American family physician, 76(7), 1005–1012
  • Saritas, I., Allahverdi, N., & Sert, I. U. (2003). A Fuzzy Expert System design for diagnosis of prostate cancer. In Proceedings of the 4th international conference conference on Computer systems and technologies: e-Learning (CompSysTech '03). Association for Computing Machinery, New York, NY, USA, 345–351. https://doi.org/10.1145/973620.973677
  • Schwartz, W. B. (1970). Medicine and the computer: The promise and problems of change. In Use and impact of computers in clinical medicine (pp. 321–335). Springer.
  • Sittig, D. F., Gardner, R. M., Pace, N. L., Morris, A. H., & Beck, E. (1989). Computerized management of patient care in a complex, controlled clinical trial in the intensive care unit. Computer Methods and Programs in Biomedicine, 30(2–3), 77–84. https://doi.org/10.1016/0169-2607(89)90060-6
  • Slaby, A. (2007). ROC analysis with Matlab. In 2007 29th International Conference on Information Technology Interfaces, IEEE, Cavtat, Croatia
  • Terribile, C., Marino, P., & Giorgio, I. (1991). A prototype expert system in gastroenterology. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society Volume 13 : 1991, Orlando, FL, USA, 1991, pp. 1337–1338
  • Tonelli, M. R. (1999). In defense of expert opinion. Academic Medicine: Journal of the Association of American Medical Colleges, 74(11), 1187–1192. https://doi.org/10.1097/00001888-199911000-00010
  • Torchio, M., Molino, G., Cavanna, A., Appendini, L., & Fornara, A. (1989). PEPTY: A knowledge-based program for assisting medical reasoning in peptic diseases. Computer Methods and Programs in Biomedicine, 28(4), 249–256. https://doi.org/10.1016/0169-2607(89)90158-2
  • Towell, G. G., & Shavlik, J. W. (1994). Knowledge-based artificial neural networks. Artificial Intelligence, 70(1–2), 119–165. https://doi.org/10.1016/0004-3702(94)90105-8
  • Umashanker, M., & Shruti, S. (2011). Traditional Indian herbal medicine used as antipyretic, antiulcer, anti-diabetic and anticancer: A review. International Journal of Research in Pharmacy and Chemistry, 1(4), 1152–1159.
  • Wilson, James Maxwell Glover, Jungner, Gunnar & World Health Organization. (‎1968)‎. Principles and practice of screening for disease / J. M. G. Wilson, G. Jungner. World Health Organization. https://apps.who.int/iris/handle/10665/37650
  • Zadeh, L. A. (1994). Fuzzy logic, neural networks, and soft computing. Communications of the ACM, 37(3), 77–85. https://doi.org/10.1145/175247.175255
  • Zolghadri, M. J., & Mansoori, E. G. (2007). Weighting fuzzy classification rules using receiver operating characteristics (ROC) analysis. Information Sciences, 177(11), 2296–2307. https://doi.org/10.1016/j.ins.2006.12.009