TY - GEN
T1 - Web-Based Decision Support System for Liver Fibrosis Prediction Using Combination of Principal Component Analysis and Artificial Neural Network
AU - Fatoni, Muhammad Hilman
AU - Muthmainnah, Nashita Khusnul
AU - Sardjono, Tri Arief
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Hepatitis has become one of the diseases that contribute to many mortality rates. Hepatitis patients are at high risk of getting Liver Fibrosis as their tissue is already in inflammation. A condition of liver fibrosis itself is where the tissue organs are producing excessive protein including collagen. This condition is worsened as the tissue are failed to regenerate and causing the blockage of the blood supply into the liver. In order to get the best treatment and prevent liver fibrosis continue to an advanced stage, early diagnosis is required. This study proposed a web-based decision support system using a combination of principal component analysis and an artificial neural network. This system is planned to classify liver fibrosis. From the principal component analysis part, which are PC-20, PC-23, and PC-27. These principal components are calculated to get better classification. And then the step continued in the artificial neural network (ANN). The best topology used in this ANN shows a classification accuracy of 96%. This application is designed web-based. It also tested using User-Centered Design to measure if the application is fit in the user's point of view. In conclusion, this web application has usability level of 'GOOD' and 0.74 usability points.
AB - Hepatitis has become one of the diseases that contribute to many mortality rates. Hepatitis patients are at high risk of getting Liver Fibrosis as their tissue is already in inflammation. A condition of liver fibrosis itself is where the tissue organs are producing excessive protein including collagen. This condition is worsened as the tissue are failed to regenerate and causing the blockage of the blood supply into the liver. In order to get the best treatment and prevent liver fibrosis continue to an advanced stage, early diagnosis is required. This study proposed a web-based decision support system using a combination of principal component analysis and an artificial neural network. This system is planned to classify liver fibrosis. From the principal component analysis part, which are PC-20, PC-23, and PC-27. These principal components are calculated to get better classification. And then the step continued in the artificial neural network (ANN). The best topology used in this ANN shows a classification accuracy of 96%. This application is designed web-based. It also tested using User-Centered Design to measure if the application is fit in the user's point of view. In conclusion, this web application has usability level of 'GOOD' and 0.74 usability points.
KW - Artificial Neural Network
KW - Decision Support System
KW - Hepatitis
KW - Liver Fibrosis
KW - Principal Component Analysis
UR - http://www.scopus.com/inward/record.url?scp=85137900324&partnerID=8YFLogxK
U2 - 10.1109/ISITIA56226.2022.9855366
DO - 10.1109/ISITIA56226.2022.9855366
M3 - Conference contribution
AN - SCOPUS:85137900324
T3 - 2022 International Seminar on Intelligent Technology and Its Applications: Advanced Innovations of Electrical Systems for Humanity, ISITIA 2022 - Proceeding
SP - 42
EP - 47
BT - 2022 International Seminar on Intelligent Technology and Its Applications
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 23rd International Seminar on Intelligent Technology and Its Applications, ISITIA 2022
Y2 - 20 July 2022 through 21 July 2022
ER -
