Fuzzy Logic Based Decision Support Systems for Medical Diagnosis

Authors

  • Mule Shrishail Basvant Associate Professor, Department of Electronics & Telecommunication Engineering, Sinhgad College of Engineering, Pune-41
  • Kamatchi K. S., Assistant Professor, Department of IT, Sathyabama Institute of Science and Technology, Chennai
  • A. Deepak Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamilnadu
  • Manish Sharma Associate Professor, Department of Computer Science & Engineering, Graphic Era Deemed to be University, Dehradun, Uttarakhand
  • 5Rakesh Kumar Department of Computer Engineering & Applications, GLA University, Mathura
  • Vijay Kumar Yadav Lloyd Institute of Engineering & Technology, Greater Noida
  • Akhil Sankhyan Lloyd Law College, Greater Noida
  • Anurag Shrivastava Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamilnadu

Keywords:

Sugeno FIS, Decision Support Systems, Fuzzy Logic, Medical Diagnosis, Mamdani FIS

Abstract

This research pioneers the integration of fuzzy rationale into choice-back frameworks for the restorative conclusion, drawing motivation from later headways within the field. Utilizing the Mamdani Fuzzy Inference Framework, the study centers on progressing symptomatic exactness and interpretability in healthcare scenarios. A comparative investigation with the Sugeno FIS gives experiences into the qualities and shortcomings of distinctive fuzzy induction frameworks. Emphasizing real-world pertinence and moral contemplations, the investigation proposes an open-source execution, cultivating collaboration and straightforwardness. Essential commitments from related studies, crossing maturing forecast, kidney disease determination, and rest apnea discovery, educate the research's direction. Future work points to extending the comparative investigation, investigating hybrid approaches, and coordinating logical manufactured insights procedures, guaranteeing the proposed framework advances with developing advances. The investigation envisions a dynamic commitment to the progressing advancement of clever choice bolster frameworks in therapeutic conclusion.

Downloads

Download data is not yet available.

References

ABDULLAH, S., ALMAGRABI, A.O. and ULLAH, I., 2023. A New Approach to Artificial Intelligent Based Three-Way Decision Making and Analyzing S-Box Image Encryption Using TOPSIS Method. Mathematics, 11(6), pp. 1559.

AL-ARS, Z. and AL-BAKRY, A., 2019. A web/mobile decision support system to improve medical diagnosis using a combination of K-Mean and fuzzy logic. Telkomnika, 17(6), pp. 3145-3154.

ALBARRAK, A.M., 2023. Determining a Trustworthy Application for Medical Data Visualizations through a Knowledge-Based Fuzzy Expert System. Diagnostics, 13(11), pp. 1916.

CASAL-GUISANDE, M., ÁLVAREZ-PAZÓ, A., CERQUEIRO-PEQUEÑO, J., BOUZA-RODRÍGUEZ, J., PELÁEZ-LOURIDO, G. and COMESAÑA-CAMPOS, A., 2023. Proposal and Definition of an Intelligent Clinical Decision Support System Applied to the Screening and Early Diagnosis of Breast Cancer. Cancers, 15(6), pp. 1711.

CASAL-GUISANDE, M., TORRES-DURÁN, M., MAR MOSTEIRO-AÑÓN, CERQUEIRO-PEQUEÑO, J., BOUZA-RODRÍGUEZ, J., FERNÁNDEZ-VILLAR, A. and COMESAÑA-CAMPOS, A., 2023. Design and Conceptual Proposal of an Intelligent Clinical Decision Support System for the Diagnosis of Suspicious Obstructive Sleep Apnea Patients from Health Profile. International Journal of Environmental Research and Public Health, 20(4), pp. 3627.

CZMIL, A., 2023. Comparative Study of Fuzzy Rule-Based Classifiers for Medical Applications. Sensors, 23(2), pp. 992.

HAI, V.P., CU, K.L., PHAN, H.K. and HA, Q.T., 2023. A Fuzzy Knowledge Graph Pairs-Based Application for Classification in Decision Making: Case Study of Preeclampsia Signs. Information, 14(2), pp. 104.

HOYOS, W., AGUILAR, J. and TORO, M., 2022. A clinical decision-support system for dengue based on fuzzy cognitive maps. Health care management science, 25(4), pp. 666-681.

IHSAN, M., SAEED, M., AGAEB, M.A. and HAMIDEN EL-WAHED KHALIFA, 2023. An algorithmic multiple attribute decision-making method for heart problem analysis under neutrosophic hypersoft expert set with fuzzy parameterized degree-based setting. PeerJ Computer Science, .

LONG, H., WANG, Z., CUI, Y., WANG, J., GAO, B., CHEN, C., ZHU, Y. and HERRE, H., 2022. A Prototype for Diagnosis of Psoriasis in Traditional Chinese Medicine. Computers, Materials, & Continua, 73(3), pp. 5197-5217.

MAQBOOL, S., IMRAN, S.B., MAQBOOL, S., RAMZAN, S. and MUHAMMAD, J.C., 2023. A Smart Sensing Technologies-Based Intelligent Healthcare System for Diabetes Patients. Sensors, 23(23), pp. 9558.

MUSTAPOEVICH, D.T., TULKUNOVNA, D.M., ULMASOVNA, L.S., PRIMOVA, H. and KIM, W., 2023. Improved Cattle Disease Diagnosis Based on Fuzzy Logic Algorithms. Sensors, 23(4), pp. 2107.

OBOT, O., ANIETIE, J., UDO, I., ATTAI, K., JOHNSON, E., UDOH, S., NWOKORO, C., AKWAOWO, C., DAN, E., UMOH, U. and FAITH-MICHAEL UZOKA, 2023. Modelling Differential Diagnosis of Febrile Diseases with Fuzzy Cognitive Map. Tropical Medicine and Infectious Disease, 8(7), pp. 352.

OTHMAN, K.M.Z. and ZEKI, N.M., 2023. Therapeutic management of diseases based on fuzzy logic system- hypertriglyceridemia as a case study. Telkomnika, 21(2), pp. 314-323.

PONISZEWSKA-MARAŃDA, A., VYNOGRADNYK, E. and MARAŃDA, W., 2023. Medical Data Transformations in Healthcare Systems with the Use of Natural Language Processing Algorithms. Applied Sciences, 13(2), pp. 682.

RASHED, B.M. and POPESCU, N., 2023. Performance Investigation for Medical Image Evaluation and Diagnosis Using Machine-Learning and Deep-Learning Techniques. Computation, 11(3), pp. 63.

WÓJCIK, W., MEZHIIEVSKA, I., PAVLOV, S.V., LEWANDOWSKI, T., VLASENKO, O.V., MASLOVSKYI, V., VOLOSOVYCH, O., KOBYLIANSKA, I., MOSKOVCHUK, O., OVCHARUK, V. and LEWANDOWSKA, A., 2023. Medical Fuzzy-Expert System for Assessment of the Degree of Anatomical Lesion of Coronary Arteries. International Journal of Environmental Research and Public Health, 20(2), pp. 979.

WU, H. and XU, Z., 2021. Fuzzy Logic in Decision Support: Methods, Applications and Future Trends. International Journal of Computers, Communications and Control, 16(1),.

YAMID FABIÁN HERNÁNDEZ-JULIO, DÍAZ-PERTUZ, L.A., PRIETO-GUEVARA, M., MAURICIO ANDRÉS BARRIOS-BARRIOS and NIETO-BERNAL, W., 2023. Intelligent Fuzzy System to Predict the Wisconsin Breast Cancer Dataset. International Journal of Environmental Research and Public Health, 20(6), pp. 5103.

Shrivastava, A., Chakkaravarthy, M., Shah, M.A..A Novel Approach Using Learning Algorithm for Parkinson’s Disease Detection with Handwritten Sketches. In Cybernetics and Systems, 2022

Shrivastava, A., Chakkaravarthy, M., Shah, M.A., A new machine learning method for predicting systolic and diastolic blood pressure using clinical characteristics. In Healthcare Analytics, 2023, 4, 100219

Shrivastava, A., Chakkaravarthy, M., Shah, M.A.,Health Monitoring based Cognitive IoT using Fast Machine Learning Technique. In International Journal of Intelligent Systems and Applications in Engineering, 2023, 11(6s), pp. 720–729

Shrivastava, A., Rajput, N., Rajesh, P., Swarnalatha, S.R., IoT-Based Label Distribution Learning Mechanism for Autism Spectrum Disorder for Healthcare Application. In Practical Artificial Intelligence for Internet of Medical Things: Emerging Trends, Issues, and Challenges, 2023, pp. 305–321

Boina, R., Ganage, D., Chincholkar, Y.D., .Chinthamu, N., Shrivastava, A., Enhancing Intelligence Diagnostic Accuracy Based on Machine Learning Disease Classification. In International Journal of Intelligent Systems and Applications in Engineering, 2023, 11(6s), pp. 765–774

Shrivastava, A., Pundir, S., Sharma, A., ...Kumar, R., Khan, A.K. Control of A Virtual System with Hand Gestures. In Proceedings - 2023 3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023, 2023, pp. 1716–1721

CASAL-GUISANDE, M., CERQUEIRO-PEQUEÑO, J., BOUZA-RODRÍGUEZ, J. and COMESAÑA-CAMPOS, A., 2023. Integration of the Wang & Mendel Algorithm into the Application of Fuzzy Expert Systems to Intelligent Clinical Decision Support Systems. Mathematics, 11(11), pp. 2469.

COLLOC, J., RELWENDÉ, A.Y., SUMMONS, P., LOUBET, L., JEAN-BERNARD CAVELIER and BRIDIER, P., 2022. A Temporal Case-Based Reasoning Platform Relying on a Fuzzy Vector Spaces Object-Oriented Model and a Method to Design Knowledge Bases and Decision Support Systems in Multiple Domains. Algorithms, 15(2), pp. 66.

EKONG, B., IFIOK, I., UDOEKA, I. and ANAMFIOK, J., 2020. Integrated Fuzzy based Decision Support System for the Management of Human Disease. International Journal of Advanced Computer Science and Applications, 11(2),.

FARAZ, M.I., ALHAMZI, G., IMTIAZ, A., MASMALI, I., SHUAIB, U., RAZAQ, A. and RAZZAQUE, A., 2023. A Decision-Making Approach to Optimize COVID-19 Treatment Strategy under a Conjunctive Complex Fuzzy Environment. Symmetry, 15(7), pp. 1370.

GERMASHEV, I.V. and DUBOVSKAYA, V.I., 2021. Application of Fuzzy Mathematics Models for Solving Medical Diagnostics Problems. Mathematical Physics and Computer Modeling, 24(4)

Downloads

Published

07.02.2024

How to Cite

Basvant, M. S. ., K. S., K. ., Deepak, A. ., Sharma, M. ., Kumar, 5Rakesh ., Yadav, V. K. ., Sankhyan, A. ., & Shrivastava, A. . (2024). Fuzzy Logic Based Decision Support Systems for Medical Diagnosis. International Journal of Intelligent Systems and Applications in Engineering, 12(15s), 01–07. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4701

Issue

Section

Research Article

Most read articles by the same author(s)

1 2 3 4 > >>