- All Journals
- Al-Qadisiyah University
- Basrah University
- Kirkuk University
- Mosul University
- The Iraqi Borad for Medical Specialization
- University of Anbar
- University of Technology
- Al Mustafa University
- Al-Zahraa University for Women
- Alnoor University
- Kunooz University
- Misan University
- Ninevah University
- Tikrit University
- University of Al-Hamdaniya
- University of Kerbala
Using Fuzzy Linear Regression Models to Identify the Factors Affecting Myocardial Infarction | ||
| Journal of University of Anbar for Pure Science | ||
| Volume 19, Issue 1 - Serial Number 19, May and June 2025, Pages 269-278 PDF (706.85 K) | ||
| Document Type: Review Paper | ||
| DOI: 10.37652/juaps.2024.150719.1274 | ||
| Authors | ||
| Wafaa S. Hasanain* ; Aya Mohammed Hussein | ||
| Department of Mathematics, College of Science, Mustansiriyah University, Baghdad, Iraq | ||
| Abstract | ||
| This study focuses on the application of many fuzzy linear regression models to analyses medical data concerning myocardial infarction, particularly with the level of the troponin, which is vital for identifying heart attacks. More specifically, the physiological indicators describing the state of the patient including blood pressure, blood sugar, creatinine, and the relationship between these indicators and the heart attack enzyme level. To accomplish the aim of study, the necessary data were obtained from the hospitals that treated patients with a diagnosis of myocardial infarction. Then the performance of many fuzzy regression methods was compared employing these data. The study shows that the fuzzy least squares procedure has the lowest mean squared error values of all the models, and the best accuracy in simulating the effect of physiological factors on the level of the heart attack enzyme. Also, this study emphasizes the importance of applying fuzzy regression in medical statistics due to the existence of uncertainty in the field and the applicability of this method to enhance predictive power and decision-making in healthcare. | ||
| Keywords | ||
| FLS; FLAR; PLR; PLRLS; BFRL | ||
| References | ||
|
[1] Vittinghoff, E., Shiboski S., Glidden D., & McCulloch C. (2005). Regression Methods in Biostatistics, Springer.
[2] Klir, G. J., & Wierman, M. J. (1999). Uncertainty Based Information, 2nd ed. ser. Studies in Fuzziness and Soft Computing,15., Heidelberg, Germany: Physica, Verlag.
[3] Cao, B. Y. (2010). Optimal Models and Methods with Fuzzy Quantities: Springer, Verlag Berlin Heidelberg.
[4] zadeh, L. A. (1965). fuzzy set, information and control. 338-353.
[5] zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning, Part 1. Information Science, 199-249.
[6] Tanaka, H. (1982). A linear regression model with fuzzy function. Journal of the Operations Research Society of Japan, 25, 162-173.
[7] Tanaka, H., Uejima, S., & Asai, K. (1982). Linear regression analysis with fuzzy model. IEEE Transactions on Systems, Man and Cybernetics, 12(6) no, 903–907.
[8] Diamond, PH. (1988). Fuzzy least squares. Information Sciences, 46(3), 141-157.
[9] Tanaka, H., Hayashi, I., & Watada, J. (1989). Possibilistic linear regression analysis for fuzzy data. European Journal of Operational Research, 40(3), 389-396.
[10] Lee, H., & Tanaka, H. (1999). Fuzzy Approximation with Non-Symmetric Fuzzy Parameters in Fuzzy Regression Analysis. Journal of the Operations Research Society of Japan, 24(1), 98-112.
[11] Nasrabadi, M., Nasrabadi, E., & Nasrabady, A. (2005). Fuzzy linear regression analysis: a multi-objective programming approach. Applied Mathematics and Computation, 163(1), 245-251.
[12] Arabpour, A. R., & Tata, M. (2008). Estimating the parameters of A fuzzy linear regression model, Iranian Journal of Fuzzy Systems, 5(2) , 1-19.
[13] Zeng, W., Feng, Q., & Li, J. (2017). Fuzzy least absolute linear regression. Applied Soft Computing, 52(5), 1009-1019.
[14] Hasanain, W. S. (2020). Assessment of Teachers' Knowledge about Tuberculosis at Primary School in Balad City. Journal of Physics: Conference Series, 159(1).
[15] Škrabánek, P., Marek, J., & Pozdílková, A. (2021). Fuzzy Regression Line. Mathematics, 9(6).
[16] Yingfang, Li, Xingxing, He., &Xueqin, Liu. (2023). Fuzzy multiple linear least squares regression analysis. Fuzzy Sets and Systems, 459, 118-143.
[17] Tanaka, H., Hayashi, I., & Watada, J. (1989). Possibilistic linear regression analysis for fuzzy data. European Journal of Operational Research, 40(3), 389-396.
[18] Chachi, J., & Taheri, M. (2016). Multiple fuzzy regression model for fuzzy input-output data. Iranian Journal of Fuzzy Systems, 13(4), 63-78.
[19] Yingfang, L., Xingxing, H., & Xueqin, L. (2003). Fuzzy multiple linear least squares regression analysis. Fuzzy Sets and Systems, 459, 118-143.
[20] Khan, M., Kumar, R., Aledaily, A. N., Kariri E., Viriyasitavat, W., Yadav, K., Dhiman, G., Kaur, A., Sharma, A., & Vimal, S. (2024). A systematic survey on implementation of fuzzy regression models for real life applications. Archives of Computational Methods in Engine, 31(1).
[21] Yen, K. K., Ghoshrya, S., & Roig, G. (1999). A linear regression model using triangular fuzzy number coefficient. Fuzzy Sets and Systems, 106, 167-177.
[22] Elaibi, W. M., Hasanain W.S., & Taha, A. R. (2022). Optimality criterion for three proposed methods to unbalansed transportation models. Italian Journal of Pure and Applied Mathematics, 48, 67–79.
[23] Hameed, A. S., Chachan, H. A., & Ali, H. J. (2023). Optimizing multi-objective problem with setup times. AIP Conference Proceedings, 2414(1). | ||
|
Statistics Article View: 480 PDF Download: 189 |
||