Design of an expert system for diagnosing eye disease using the certainty factor method

Authors

  • Birch Shah Universidade NOVA de Lisboa, Caparica, Portugal Author
  • Zeeshan Universidade NOVA de Lisboa, Caparica, Portugal Author
  • Asghar Sadiq Universidade Nacional Timor Lorosa'e, Dili, Timor Leste Author
  • Übeyli Ung Universidade Nacional Timor Lorosa'e, Dili, Timor Leste Author
  • Shanbhag Gilmore Universidad de Cádiz, Cádiz, Spain Author

DOI:

https://doi.org/10.35335/vw11e535

Keywords:

Artificial Intelligence, Certainty Factor Method, Diagnosing Eye Diseases, Expert System, Medical Diagnosis

Abstract

Accurate eye disease diagnosis is essential for therapy and vision preservation. Complexity and diversity of symptoms often confound established diagnostic methods. We suggest designing an expert system for eye illness diagnosis utilizing the certainty factor method, which handles ambiguous and imprecise medical diagnoses. A systematic technique to improve diagnostic accuracy is proposed in this research to bridge medical experience and computational reasoning. The research begins with a mathematical framework for symptoms, test data, and diagnostic conclusions. The system uses medical-inspired rule-based inference to aid evidence-based reasoning. The certainty factor technique quantifies diagnosis confidence, ensuring transparency and justifiability. A numerical example shows how to apply the strategy. The simple example shows how the expert system can analyze various criteria and make well-supported diagnoses. It emphasizes symptom analysis, test results, and the certainty factor method's capacity to handle uncertainty. This research emphasizes the interaction between artificial intelligence and medical competence and is conceptual. Real-world application involves medical practitioner participation, intensive testing, and patient data validation. This research advances medical diagnostic tools by combining computational and clinical knowledge. It symbolizes a shift toward efficient, accurate, and transparent diagnostic methods to improve patient care and healthcare.

References

Abràmoff, M. D., Lavin, P. T., Birch, M., Shah, N., & Folk, J. C. (2018). Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ Digital Medicine, 1(1), 39.

Adem, K. (2018). Exudate detection for diabetic retinopathy with circular Hough transformation and convolutional neural networks. Expert Systems with Applications, 114, 289–295.

Ahmed, Z., Mohamed, K., Zeeshan, S., & Dong, X. (2020). Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine. Database, 2020, baaa010.

Akerkar, R., & Sajja, P. (2009). Knowledge-based systems. Jones & Bartlett Publishers.

Alther, M., & Reddy, C. K. (2015). Clinical Decision Support Systems.

Azim, T., Jaffar, M. A., & Mirza, A. M. (2014). Fully automated real time fatigue detection of drivers through fuzzy expert systems. Applied Soft Computing, 18, 25–38.

Berg, M. (1997). Rationalizing medical work: decision-support techniques and medical practices. MIT press.

Chandrasekaran, B. (1986). Generic tasks in knowledge-based reasoning: High-level building blocks for expert system design. IEEE Intelligent Systems, 1(03), 23–30.

Dlamini, Z., Francies, F. Z., Hull, R., & Marima, R. (2020). Artificial intelligence (AI) and big data in cancer and precision oncology. Computational and Structural Biotechnology Journal, 18, 2300–2311.

Faulkner, E., Holtorf, A.-P., Liu, C. Y., Lin, H., Biltaj, E., Brixner, D., Barr, C., Oberg, J., Shandhu, G., & Siebert, U. (2020). Being precise about precision medicine: what should value frameworks incorporate to address precision medicine? A report of the personalized precision medicine special interest group. Value in Health, 23(5), 529–539.

Frosch, D. L., & Kaplan, R. M. (1999). Shared decision making in clinical medicine: past research and future directions. American Journal of Preventive Medicine, 17(4), 285–294.

Gamage, S., & Samarabandu, J. (2020). Deep learning methods in network intrusion detection: A survey and an objective comparison. Journal of Network and Computer Applications, 169, 102767.

Gerrity, M. S., Earp, J. A. L., DeVellis, R. F., & Light, D. W. (1992). Uncertainty and professional work: perceptions of physicians in clinical practice. American Journal of Sociology, 97(4), 1022–1051.

Ghosh, A. K. (2004). On the challenges of using evidence-based information: the role of clinical uncertainty. Journal of Laboratory and Clinical Medicine, 144(2), 60–64.

Hall, L. O., & Kandel, A. (1992). The evolution from expert systems to fuzzy expert systems. Fuzzy Expert Systems, 8(1), 3–21.

Kiani, R., & Shadlen, M. N. (2009). Representation of confidence associated with a decision by neurons in the parietal cortex. Science, 324(5928), 759–764.

Malik, S., Kanwal, N., Asghar, M. N., Sadiq, M. A. A., Karamat, I., & Fleury, M. (2019). Data driven approach for eye disease classification with machine learning. Applied Sciences, 9(14), 2789.

Muludi, K., Suharjo, R., Admi Syarif, A. S., & Ramadhani, F. (2018). Implementation of forward chaining and certainty factor method on android-based expert system of tomato diseases identification. International Journal of Advanced Computer Science and Applications (IJACSA), 9(9), 451–456.

Musen, M. A., Middleton, B., & Greenes, R. A. (2021). Clinical decision-support systems. In Biomedical informatics: computer applications in health care and biomedicine (pp. 795–840). Springer.

Osei, K. A., Cox, S. M., & Nichols, K. K. (2020). Dry eye disease practice in Ghana: diagnostic perspectives, treatment modalities, and challenges. Optometry and Vision Science, 97(3), 137–144.

Rodrigues Jr, J. F., Paulovich, F. V, De Oliveira, M. C. F., & de Oliveira Jr, O. N. (2016). On the convergence of nanotechnology and Big Data analysis for computer-aided diagnosis. Nanomedicine, 11(8), 959–982.

Sammarco, A. (2001). Perceived social support, uncertainty, and quality of life of younger breast cancer survivors. Cancer Nursing, 24(3), 212–219.

Schmidt, R., Pollwein, B., & Gierl, L. (1999). Experiences with case-based reasoning methods and prototypes for medical knowledge-based systems. Artificial Intelligence in Medicine: Joint European Conference on Artificial Intelligence in Medicine and Medical Decision Making, AIMDM’99 Aalborg, Denmark, June 20–24, 1999 Proceedings, 124–132.

Sikchi, S. S., Sikchi, S., & Ali, M. S. (2013). Design of fuzzy expert system for diagnosis of cardiac diseases. International Journal of Medical Science and Public Health, 2(1), 56–61.

Tavana, M., & Hajipour, V. (2020). A practical review and taxonomy of fuzzy expert systems: methods and applications. Benchmarking: An International Journal, 27(1), 81–136.

Ting, D. S. W., Cheung, C. Y.-L., Lim, G., Tan, G. S. W., Quang, N. D., Gan, A., Hamzah, H., Garcia-Franco, R., San Yeo, I. Y., & Lee, S. Y. (2017). Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. Jama, 318(22), 2211–2223.

Tuani, A. F., Keedwell, E., & Collett, M. (2020). Heterogenous Adaptive Ant Colony Optimization with 3-opt local search for the Travelling Salesman Problem. Applied Soft Computing Journal, xxxx, 106720. https://doi.org/10.1016/j.asoc.2020.106720

Übeyli, E. D. (2008). Adaptive neuro-fuzzy inference system employing wavelet coefficients for detection of ophthalmic arterial disorders. Expert Systems with Applications, 34(3), 2201–2209.

Ung, L., Bispo, P. J. M., Shanbhag, S. S., Gilmore, M. S., & Chodosh, J. (2019). The persistent dilemma of microbial keratitis: Global burden, diagnosis, and antimicrobial resistance. Survey of Ophthalmology, 64(3), 255–271.

Published

2022-06-30

How to Cite

Design of an expert system for diagnosing eye disease using the certainty factor method. (2022). Vertex, 11(2), 56-63. https://doi.org/10.35335/vw11e535

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