TY - GEN
T1 - Machine Learning Models to Classify Normal and Fibrotic Mouse Liver Model using Dielectric Properties
AU - Samaddar, Poulami
AU - Samadder, Poushali
AU - Baraskar, Bhavana
AU - Anvekar, Priyanka
AU - Khanal, Shalil
AU - Gaddam, Sunil
AU - Roy, Sayan
AU - Mitra, Dipankar
AU - Kostallari, Enis
AU - Arunachalam, Shivaram P.
N1 - Funding Information:
ACKNOWLEDGMENT This work was supported in parts by GIH Division’s 2021 Research Innovation Award for Shivaram P. Arunachalam. This work was also supported by the GIH Division, Department of Medicine for the Microwave Engineering & Imaging Laboratory (MEIL).
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - In this work dielectric permittivity was used to classify between normal and fibrotic mouse liver model. Data set was binary classified using six machine learning models i.e., Logistic Regression, Support vector classification, K-Nearest Neighbors Classifier, Decision Tree Classifier, Random Forest Classifier and Naive Bayes. Based on the accuracy and AUC score, K-Nearest Neighbors Classifier shows the best performance. This is a preliminary study to understand the feasibility of using electrical property such as complex permittivity as a biomarker to classify normal and fibrotic liver of diseased mouse model using machine learning models. The result of this work shows that the machine learning models can be used to distinguish between healthy and diseased liver with more than 80% accuracy. This technology shows immense possibility to expand in future to quantify disease severity and develop non-invasive diagnostic tools.
AB - In this work dielectric permittivity was used to classify between normal and fibrotic mouse liver model. Data set was binary classified using six machine learning models i.e., Logistic Regression, Support vector classification, K-Nearest Neighbors Classifier, Decision Tree Classifier, Random Forest Classifier and Naive Bayes. Based on the accuracy and AUC score, K-Nearest Neighbors Classifier shows the best performance. This is a preliminary study to understand the feasibility of using electrical property such as complex permittivity as a biomarker to classify normal and fibrotic liver of diseased mouse model using machine learning models. The result of this work shows that the machine learning models can be used to distinguish between healthy and diseased liver with more than 80% accuracy. This technology shows immense possibility to expand in future to quantify disease severity and develop non-invasive diagnostic tools.
KW - Artificial Intelligence
KW - complex permittivity
KW - dielectric properties
KW - fibrosis
KW - liver
KW - machine learning
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U2 - 10.1109/BIBM55620.2022.9995397
DO - 10.1109/BIBM55620.2022.9995397
M3 - Conference contribution
AN - SCOPUS:85146706750
T3 - Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022
SP - 2696
EP - 2703
BT - Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022
A2 - Adjeroh, Donald
A2 - Long, Qi
A2 - Shi, Xinghua
A2 - Guo, Fei
A2 - Hu, Xiaohua
A2 - Aluru, Srinivas
A2 - Narasimhan, Giri
A2 - Wang, Jianxin
A2 - Kang, Mingon
A2 - Mondal, Ananda M.
A2 - Liu, Jin
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022
Y2 - 6 December 2022 through 8 December 2022
ER -