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Reseach Article

A Cognitive Inference Approach for Developing Medical Diagnostic Expert Systems

by Ashish Chandiok, D. K. Chaturvedi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 179 - Number 21
Year of Publication: 2018
Authors: Ashish Chandiok, D. K. Chaturvedi
10.5120/ijca2018916376

Ashish Chandiok, D. K. Chaturvedi . A Cognitive Inference Approach for Developing Medical Diagnostic Expert Systems. International Journal of Computer Applications. 179, 21 ( Feb 2018), 1-7. DOI=10.5120/ijca2018916376

@article{ 10.5120/ijca2018916376,
author = { Ashish Chandiok, D. K. Chaturvedi },
title = { A Cognitive Inference Approach for Developing Medical Diagnostic Expert Systems },
journal = { International Journal of Computer Applications },
issue_date = { Feb 2018 },
volume = { 179 },
number = { 21 },
month = { Feb },
year = { 2018 },
issn = { 0975-8887 },
pages = { 1-7 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume179/number21/28989-2018916376/ },
doi = { 10.5120/ijca2018916376 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:56:01.613001+05:30
%A Ashish Chandiok
%A D. K. Chaturvedi
%T A Cognitive Inference Approach for Developing Medical Diagnostic Expert Systems
%J International Journal of Computer Applications
%@ 0975-8887
%V 179
%N 21
%P 1-7
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Cognitive approaches are nowadays the most popular and widely used means of computing information due to its human-like sensing, comprehending, and action. The cognitive system can handle diverse data, uses modern technologies like natural language processing, machine learning, semantics, and decision support. It can have human interaction behavior with contextual data handling. The decisions are also not made on fixed rules, but human-like weight based judgments. Expert systems (ES’s) are intelligent software tools that use acquired knowledge from experts in a specific domain to offer assistance to its users over a dialog or a query talks conducted between the user and the ES application software. This research work is for implementing a cognitive approach for determining diagnosis decisions providing expert system services. Cognitive strategies frequently denoted as ”Human-like intelligent Computing Method” using restricted processing, storing and displaying skills. This paper presents a new diagnosis problem resolving model grounded in the investigation of the relationships among symptoms and illnesses in the form of certainty and severity elements. The article also acquaints with a modified knowledge representation appropriate for a cognitive system with limited structured data handling capabilities using XML language. The proposed model thrived and tested in the domain of medicals like fever (Flu, Pneumonia, and Cold Fever) using real knowledge base acquired by the mayo clinic. The earned consequences establish the strength and competence of the planned model.

References
  1. Janice S Aikins, John C Kunz, Edward H Shortliffe, and Robert J Fallat. Puff: an expert system for interpretation of pulmonary function data. Computers and biomedical research, 16(3):199–208, 1983.
  2. Mohammad-R Akbarzadeh-T and Majid Moshtagh- Khorasani. A hierarchical fuzzy rule-based approach to aphasia diagnosis. Journal of Biomedical Informatics, 40(5):465–475, 2007.
  3. Elias M Awad and Dustin Huntington. Building expert systems: principles, procedures, and applications.West Publishing Co., 1996.
  4. Fatih Bas¸c¸iftc¸i and Hayri ?Incekara. Web based medical decision support system application of coronary heart disease diagnosis with boolean functions minimization method. Expert Systems with Applications, 38(11):14037–14043, 2011.
  5. I Bonfa, C Maioli, F Sarti, GL Milandri, and PR Dal Monte. Hermes: an expert system for the prognosis of hepatic diseases. In Artificial Neural Networks and Expert Systems, 1993. Proceedings., First New Zealand International Two- Stream Conference on, pages 240–246. IEEE, 1993.
  6. Aicha Boutorh and Ahmed Guessoum. Complex diseases snp selection and classification by hybrid association rule mining and artificial neural networkbased evolutionary algorithms. Engineering Applications of Artificial Intelligence, 51:58–70, 2016.
  7. Ashish Chandiok and DK Chaturvedi. Cognitive decision support system for medical diagnosis. In Computational Techniques in Information and Communication Technologies (ICCTICT), 2016 International Conference on, pages 337–342. IEEE, 2016.
  8. Wei-Ling Chen, Chung-Dann Kan, Chia-Hung Lin, and Tainsong Chen. A rule-based decision-making diagnosis system to evaluate arteriovenous shunt stenosis for hemodialysis treatment of patients using fuzzy petri nets. IEEE Journal of Biomedical and Health Informatics, 18(2):703–713, 2014.
  9. Michael Cherkassky. Application of machine learning methods to medical diagnosis. Chance, 22(1):42–50, 2009.
  10. Yu-Liang Chi, Tsang-Yao Chen, and Wan-Ting Tsai. A chronic disease dietary consultation system using owl-based ontologies and semantic rules. Journal of biomedical informatics, 53:208–219, 2015.
  11. Nassim Douali, Huszka Csaba, Jos De Roo, Elpiniki I Papageorgiou, and Marie-Christine Jaulent. Diagnosis support system based on clinical guidelines: comparison between casebased fuzzy cognitive maps and bayesian networks. Computer methods and programs in biomedicine, 113(1):133– 143, 2014.
  12. Francesco Gagliardi. Instance-based classifiers applied to medical databases: diagnosis and knowledge extraction. Artificial intelligence in medicine, 52(3):123–139, 2011.
  13. Joseph C Giarratano and Gary Riley. Expert systems: principles and programming. Brooks/Cole Publishing Co., 1989.
  14. Donna L Hudson and Maurice E Cohen. Fuzzy logic in medical expert systems. IEEE Engineering in Medicine and Biology Magazine, 13(5):693–698, 1994.
  15. Borna Jafarpour, Samina Raza Abidi, and Syed Sibte Raza Abidi. Exploiting semantic web technologies to develop owlbased clinical practice guideline execution engines. IEEE journal of biomedical and health informatics, 20(1):388–398, 2016.
  16. Ali Keles¸ and Ayt¨urk Keles¸. Estdd: Expert system for thyroid diseases diagnosis. Expert Systems with Applications, 34(1):242–246, 2008.
  17. Ali Keles¸, Ayt¨urk Keles¸, and U?gur Yavuz. Expert system based on neuro-fuzzy rules for diagnosis breast cancer. Expert systems with applications, 38(5):5719–5726, 2011.
  18. K Ashwin Kumar, Yashwardhan Singh, and Sudip Sanyal. Hybrid approach using case-based reasoning and rule-based reasoning for domain independent clinical decision support in icu. Expert Systems with Applications, 36(1):65–71, 2009.
  19. Chang-Shing Lee and Mei-Hui Wang. A fuzzy expert system for diabetes decision support application. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 41(1):139–153, 2011.
  20. Chunhong Lu, Zhaomin Zhu, and Xiaofeng Gu. An intelligent system for lung cancer diagnosis using a new genetic algorithm based feature selection method. Journal of medical systems, 38(9):97, 2014.
  21. P Mohapatra, Sreejit Chakravarty, and Pradipta K Dash. An improved cuckoo search based extreme learning machine for medical data classification. Swarm and Evolutionary Computation, 24:25–49, 2015.
  22. Elpiniki I Papageorgiou. A new methodology for decisions in medical informatics using fuzzy cognitive maps based on fuzzy rule-extraction techniques. Applied Soft Computing, 11(1):500–513, 2011.
  23. Matthias Samwald, Jose Antonio Mi˜narro Gim´enez, Richard D Boyce, Robert R Freimuth, Klaus-Peter Adlassnig, and Michel Dumontier. Pharmacogenomic knowledge representation, reasoning and genome-based clinical decision support based on owl 2 dl ontologies. BMC medical informatics and decision making, 15(1):12, 2015.
  24. Rainer Schmidt, Stefania Montani, Riccardo Bellazzi, Luigi Portinale, and Lothar Gierl. Cased-based reasoning for medical knowledge-based systems. International Journal of Medical Informatics, 64(2-3):355–367, 2001.
  25. Edward Shortliffe. Computer-based medical consultations: MYCIN, volume 2. Elsevier, 2012.
  26. Edward H Shortliffe. Medical expert systemsknowledge tools for physicians. Western Journal of Medicine, 145(6):830, 1986.
  27. Ruxandra Stoean and Catalin Stoean. Modeling medical decision making by support vector machines, explaining by rules of evolutionary algorithms with feature selection. Expert Systems with Applications, 40(7):2677–2686, 2013.
  28. William van Melle, Edward H Shortliffe, and Bruce G Buchanan. Emycin: A knowledge engineers tool for constructing rule-based expert systems. Rule-based expert systems: The MYCIN experiments of the Stanford Heuristic Programming Project, pages 302–313, 1984.
  29. Xiaowei Zhang, Bin Hu, Xu Ma, Philip Moore, and Jing Chen. Ontology driven decision support for the diagnosis of mild cognitive impairment. Computer methods and programs in biomedicine, 113(3):781–791, 2014.
  30. Yi-Fan Zhang, Ling Gou, Yu Tian, Tian-Chang Li, Mao Zhang, and Jing-Song Li. Design and development of a sharable clinical decision support system based on a semantic web service framework. Journal of medical systems, 40(5):118, 2016.
Index Terms

Computer Science
Information Sciences

Keywords

Cognitive Expert System tools Medical Expert Systems Knowledge Representation Problem Resolving Modeling