An Expert System for Screening and Prognosis of Diseases: An Instance of Healthcare Management
Keywords:
Disease Classification, Clinical Decision Support, Prognosis, Artificial Intelligence, Expert System, Programming in LogicAbstract
The background of this study is that a Medical Expert System made in Visual Prolog is proposed. This expert system makes a differential diagnosis among heart, lung, kidney, skin, and brain diseases. This system is designed to give help to a medical expert in making the appropriate diagnosis of a patient. Based on a patient's symptoms and medical background, SWI Prolog offers the diagnosis through the declarative knowledge representation methodologies. On the basis of the diagnosis and current medical regulations, it might also offer other treatment options. Medical data analysis using SWI Prolog is used to spot trends or patterns in patient outcomes or disease development. Making better-educated choices concerning patient care and treatment could be made easier by healthcare providers as a result. An SWI Prolog-based medical expert system's output will be influenced by the quality of the data and code used to generate it, as well as by the medical specialists that worked on its design and implementation. The creation of more sophisticated expert systems can be particularly beneficial for early disease detection, helping to reduce the burden of diseases by detecting them more accurately and efficiently.
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Abdullah, W., & Nusari, M. (2019). The relationship between nurses’ job satisfaction and nurses’ performance in the public health care sector in Yemen. International Journal of Management and Human Science (IJMHS), 3(2), Article 2.
Ali Raza, M. A., Liaqat, M. S., & Shoaib, M. (2019). A Fuzzy Expert System Design for Diagnosis of Skin Diseases. 2019 2nd International Conference on Advancements in Computational Sciences (ICACS), 1–7. https://doi.org/10.23919/ICACS.2019.8689140
Alopecia areata. (2023). In Wikipedia. https://en.wikipedia.org/w/index.php?title=Alopecia_areata&oldid=1146649766#Signs_and_symptoms
Bhowmik, D. (2021). Covid-19: recession, poverty and inequality and redistribution. International Journal on Recent Trends in Business and Tourism (IJRTBT), 5(1), Article 1. https://doi.org/10.31674/ijrtbt.2021.v05i01.003
Bokolo, A. J. (2021). Application of telemedicine and eHealth technology for clinical services in response to COVID‑19 pandemic. Health and Technology, 11(2), 359–366. https://doi.org/10.1007/s12553-020-00516-4
Chatterjee, S., Dey, D., Munshi, S., &Gorai, S. (2021). Dermatological expert system implementing the ABCD rule of dermoscopy for skin disease identification. Expert Systems with Applications, 167, 114204. https://doi.org/10.1016/j.eswa.2020.114204
Das, S., & Sanyal, M. (2019). Application of AI and Soft Computing in Healthcare: A review and Speculation. International Journal of Scientific & Technology Research, VOLUME 8, 1786–1806.
Das, S., & Sanyal, M. K. (2020). Machine intelligent diagnostic system (MIDs): An instance of medical diagnosis of tuberculosis. Neural Computing and Applications, 32(19), 15585–15595. https://doi.org/10.1007/s00521-020-04894-8
Das, S., Biswas, S., Paul, A., & Dey, A. (2018). AI Doctor: An intelligent approach for medical diagnosis. In Industry Interactive Innovations in Science, Engineering and Technology: Proceedings of the International Conference, I3SET 2016 (pp. 173-183). Springer Singapore. https://doi.org/10.1007/978-981-10-3953-9_17
Das, S., Dey, A., Pal, A., & Roy, N. (2015). Applications of Artificial Intelligence in Machine Learning: Review and Prospect. International Journal of Computer Applications, 115(9), 31–41. https://doi.org/10.5120/20182-2402
Das, S., Mondal, D., Roy, P., Das, T., Roy, R., & Majumdar, D. (2023). A Comparative Analysis and Prediction of Ovarian Cancer using AI Approach. Asia-Pacific Journal of Management and Technology (AJMT), 3(3), Article 3. https://doi.org/10.46977/apjmt.2023.v03i03.003
Das, S., Sanyal, M., & Datta, D. (2020a). Artificial Intelligent Embedded Doctor (AIEDr.): A Prospect of Low Back Pain Diagnosis. 4, 34–56. https://doi.org/10.4018/IJBDAH.2019070103
Das, S., Sanyal, M., & Datta, D. (2020b). Artificial Intelligent Reliable Doctor (AIRDr.): Prospect of Disease Prediction Using Reliability. In Studies in Computational Intelligence (pp. 21–42). https://doi.org/10.1007/978-981-13-7334-3_3
Das, S., Sanyal, M., Datta, D., & Biswas, A. (2018b). AISLDr: Artificial Intelligent Self-learning Doctor. In V. Bhateja, C. A. CoelloCoello, S. C. Satapathy, & P. K. Pattnaik (Eds.), Intelligent Engineering Informatics. 695, 79–90. Springer Singapore. https://doi.org/10.1007/978-981-10-7566-7_9
Jeddi, F. R., Arabfard, M., Arabkermany, Z., &Gilasi, H. (2016). The Diagnostic Value of Skin Disease Diagnosis Expert System. Acta Informatica Medica, 24(1), 30–33. https://doi.org/10.5455/aim.2016.24.30-33
Malviya, L., Chittora, P., Chakrabarti, P., Vyas, R. S., & Poddar, S. (2021). WITHDRAWN: Backorder prediction in the supply chain using machine learning. Materials Today: Proceedings. https://doi.org/10.1016/j.matpr.2020.11.558
Mayo Clinic.(2023a). Acne—Symptoms and causes. https://www.mayoclinic.org/diseases-conditions/acne/symptoms-causes/syc-20368047
Mayo Clinic. (2023b). Atopic dermatitis (eczema)—Symptoms and causes. https://www.mayoclinic.org/diseases-conditions/atopic-dermatitis-eczema/symptoms-causes/syc-20353273
Połap, D., Winnicka, A., Serwata, K., Kęsik, K., &Woźniak, M. (2018). An Intelligent System for Monitoring Skin Diseases. Sensors (Basel, Switzerland), 18(8), 2552. https://doi.org/10.3390/s18082552
WebMD Editorial Contributors. (2023). 5 Signs & Symptoms of Psoriasis. https://www.webmd.com/skin-problems-and-treatments/psoriasis/psoriasis-signs-symptoms
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