Fuzzy-based Decision for Coronary Heart Disease Diagnosis: Systematic Literature Review
DOI:
https://doi.org/10.21512/emacsjournal.v3i2.6939Keywords:
Coronary Heart Disease, Fuzzy-based System, Medical DiagnosisAbstract
Coronary heart disease is usually caused by a buildup of fatty material and plaque inside the coronary arteries. The death rate caused by coronary heart diseases is threatening around the world. For the past two decades, most of the people from developing countries are suffering from heart disease. Diagnosing these diseases at earlier stages helps patients reduce the risk of death and also in reducing the cost of treatment. Decisions in medical diagnosis are mostly taken by expert’s experiences. In many cases, not all the expert’s experiences contribute towards effective diagnosis of a disease. Many alternative methods have been suggested for medical diagnosis in the healthcare domain. However, evaluating the functionality of coronary heart diseases diagnosis systems remains challenging. The purpose of this paper is to perform a study on literature related to fuzzy-based decision for diagnosis of coronary heart disease. Accordingly, the research gathered studies related to fuzzy-based decision for diagnosis of coronary heart disease between the periode 2016-2021.
Plum Analytics
References
Laftah, Hussin Attya; Oleiwi, Wed Kadhim. (2017). A Fuzzy Petri Nets System for Heart Disease Diagnosis. Journal of Babylon University, Pure and Applied Sciences, 25.
Kasbe, T; Pippal, R. S. (2017). Design of Heart Disease Diagnosis System using Fuzzy Logic. International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS-2017), 3183–3187.
Kahtan, H; Zamli, K. Z; Fatthi, W. N; Abdullah, A; Abdulleteef, Mr; Kamarulzaman, N. S. (2018). Heart Disease Diagnosis System Using Fuzzy Logic. ICSCA 2018: Proceedings of the 2018 7th International Conference on Software and Computer Applications,
Umasankar, P; Thiagarasu, V. (2018). Decision Support System for Heart Disease Diagnosis Using Interval Vague Set and Fuzzy Association Rule Mining. Fourth International Conference on Devices, Circuits and Systems (ICDCS'18), 223-227.
Uyar, Kaan; İlhan, Ahmet. (2017). Diagnosis of heart disease using genetic algorithm based trained recurrent fuzzy neural networks. Procedia Computer Science, 120, 588–593.
Reddy, G. T; Reddy, M. P. K; Lakshmanna, K; Rajput, D. S; Kaluri, R; Srivastava, G. (2019). Hybrid genetic algorithm and a fuzzy logic classifier for heart disease diagnosis. Evolutionary Intelligence.
Alqudah, Ali Mohammad (2017). Fuzzy expert system for coronary heart disease diagnosis in Jordan. Health and Technology, 7(2-3), 215–222.
Hachesu, P. R; Soufi, M. D; Khara, R; Moftian, N; Soltani, S. T. (2019). A fuzzy mobile decision support system for diagnosing of the angiographic status of heart disease. IEngineering and Applied Science Research.
Naseer, Iftikhar; Khan, Bilal Shoaib; Saqib, Shazia; Tahir, Syed Nadeem; Tariq, Sheraz; Akhter, Muhammad Saleem. (2020). Diagnosis Heart Disease Using Mamdani Fuzzy Inference Expert System. EAI Endorsed Transactions on Scalable Information Systems, 7(26).
Reddy, G. T; Khare, N. (2017). An Efficient System for Heart Disease Prediction Using Hybrid OFBAT with Rule-Based Fuzzy Logic Model. Journal of Circuits, Systems and Computers, 26(04), 1750061.
Hassan, Nasruddin; Sayed, Osama Rashed; Khalil, Ahmed Mostafa; Ghany, Mohamed Abdel. (2016). Fuzzy Soft Expert System in Prediction of Coronary Artery Disease. International Journal of Fuzzy Systems, 19(5), 1546–1559.
Jain, Prerna; Kaur, Amandeep. (2019). A Fuzzy Expert System for Coronary Artery Disease Diagnosis. ICAICR '19: Proceedings of the Third International Conference on Advanced Informatics for Computing Research, 47, 1-6.
Iancu, I. (2018). Heart disease diagnosis based on mediative fuzzy logic. Artificial Intelligence in Medicine, 89, 51–60.
Maomeri, S; Samadinai, N. (2018). Diagnosis of Coronary Artery Disease via a Novel Fuzzy Expert System Optimized by Cuckoo Search. IJE TRANSACTIONS, Vol. 31, No. 12, 2028-2036.
Sabahi, F. (2018). Bimodal fuzzy analytic hierarchy process (BFAHP) for coronary heart disease risk assessment. Journal of Biomedical Informatics, 83, 204–216.
Paul, A. K; Shill, P. C; Rabin, M. R. I; Murase, K. (2017). Adaptive weighted fuzzy rule-based system for the risk level assessment of heart disease. Applied Intelligence, 48(7), 1739–1756.
Sharma, P., & Saxena, K. (2017). Application of fuzzy logic and genetic algorithm in heart disease risk level prediction. International Journal of System Assurance Engineering and Management, 8(S2), 1109–1125.
Krishnaiah, V; Narsimha, G; Chandra, N. S. (2016). Heart Disease Prediction System using Data Mining Techniques and Intelligent Fuzzy Approach: A Review. International Journal of Computer Applications (0975 – 8887) Volume 136 – No.2.
Nilashi, M; Ahmadi, H; Manaf, A. A; Rashid, T. A; Samad, S; Shahmoradi, L; Aljojo, N; Akbari, E. (2020). Coronary Heart Disease Diagnosis Through Self-Organizing Map and Fuzzy Support Vector Machine with Incremental Updates. International Journal of Fuzzy Systems.
Paul, A. K; Shill, P. C; Rabin, M. R. I; Akhand, M. A. H. (2016). Genetic algorithm based fuzzy decision support system for the diagnosis of heart disease. 2016 5th International Conference on Informatics, Electronics and Vision (ICIEV).
Downloads
Published
Issue
Section
License
Copyright (c) 2021 Engineering, MAthematics and Computer Science (EMACS) Journal
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
a. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License - Share Alike that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
b. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
c. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
USER RIGHTS
All articles published Open Access will be immediately and permanently free for everyone to read and download. We are continuously working with our author communities to select the best choice of license options, currently being defined for this journal as follows: Creative Commons Attribution-Share Alike (CC BY-SA)