TY - JOUR
T1 - Design of biodegradable Mg alloy for abdominal aortic aneurysm repair (AAAR) using ANFIS regression model
AU - Hammam, Rania E.
AU - Solyman, Ahmed A.A.
AU - Alsharif, Mohammed H.
AU - Uthansakul, Peerapong
AU - Deif, Mohanad A.
N1 - Funding Information:
This work was supported in part by the Suranaree University of Technology (SUT) Research and Development Funds, and in part by the Thailand Science Research and Innovation (TSRI).
Publisher Copyright:
© 2013 IEEE.
PY - 2022/2/28
Y1 - 2022/2/28
N2 - Abdominal aortic aneurysm (AAA) is among the most widespread and dangerous diseases that may cause death. Recently, Endovascular Aneurysm Repair outperformed open aortic surgery, since it is a safe and reliable technique where a stent graft system is placed within the aortic aneurysm. It was aimed to design an Mg biodegradable alloy with bio-friendly alloying elements that enhance the corrosion resistance and mechanical properties of the alloy for the design of stents for Abdominal Aortic Aneurysm (AAA) repair. Adaptive Neuro-Fuzzy Inference System (ANFIS) was proposed for the design of the Mg alloy and compared to other traditional machine learning regression models (Multiple Linear Regression (MLR) and Gradient Boosting (GB). The dataset utilized in this work consisted of 600 samples of Mg alloys that were collected from the mat web database and additional papers from Google Scholar. The results revealed the superior prediction capability of the ANFIS model since it attained maximum R2 scores of 0.926, 0.958, and 0.988 for the prediction of UTS, YS, and Ductility respectively. Furthermore, the ANFIS model was capable of designing an Mg biodegradable alloy having a UTS, YS, and Ductility of 346.148 Mpa, 230.8 Mpa, and 15.4% respectively which are excellent mechanical properties satisfying vascular stents requirements The ANFIS model can be further applied to speed up the design of other alloys in the future for various medical applications, reducing the time, cost, and effort of large searching space.
AB - Abdominal aortic aneurysm (AAA) is among the most widespread and dangerous diseases that may cause death. Recently, Endovascular Aneurysm Repair outperformed open aortic surgery, since it is a safe and reliable technique where a stent graft system is placed within the aortic aneurysm. It was aimed to design an Mg biodegradable alloy with bio-friendly alloying elements that enhance the corrosion resistance and mechanical properties of the alloy for the design of stents for Abdominal Aortic Aneurysm (AAA) repair. Adaptive Neuro-Fuzzy Inference System (ANFIS) was proposed for the design of the Mg alloy and compared to other traditional machine learning regression models (Multiple Linear Regression (MLR) and Gradient Boosting (GB). The dataset utilized in this work consisted of 600 samples of Mg alloys that were collected from the mat web database and additional papers from Google Scholar. The results revealed the superior prediction capability of the ANFIS model since it attained maximum R2 scores of 0.926, 0.958, and 0.988 for the prediction of UTS, YS, and Ductility respectively. Furthermore, the ANFIS model was capable of designing an Mg biodegradable alloy having a UTS, YS, and Ductility of 346.148 Mpa, 230.8 Mpa, and 15.4% respectively which are excellent mechanical properties satisfying vascular stents requirements The ANFIS model can be further applied to speed up the design of other alloys in the future for various medical applications, reducing the time, cost, and effort of large searching space.
KW - abdominal aortic aneurysm
KW - ANFIS
KW - biodegradable
KW - gradient boosting
KW - Mg alloy
U2 - 10.1109/ACCESS.2022.3155645
DO - 10.1109/ACCESS.2022.3155645
M3 - Article
AN - SCOPUS:85125733794
SN - 2169-3536
VL - 10
SP - 28579
EP - 28589
JO - IEEE Access
JF - IEEE Access
ER -