TY - GEN
T1 - Prediction of foot risk classification for Type II Diabetic through image analysis
AU - Sheikh, Mehewish Musheer
AU - Balachandra, Mamatha
AU - Narendra, V. G.
AU - Maiya, Arun G.
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - The global prevalence of diabetes mellitus has increased. The use of electronic platform devices has grown in popularity due to their low cost and ease of use. Despite their benefits, however, there remain concerns about their accuracy and precision. The objective of the study is to determine the accuracy and precision of the Win-track platform. In a cross-sectional study, 49 male patients' data were collected. Based on the pressure asserted, the data were further classified into different stages from 1 to 4. The study used four different types of classifiers (Logistic, Multi-layer Perceptron, Simple Logistic Regression and Meta-logit Boost) to check the accuracy. The result shown for all the classifiers was positive with Meta-logit Boost giving the higher Mathews correlation coefficient (MCC) (stage 1=1, stage 2=1, stage 3=0.904 and stage 4=0.912) and highest correctly classified instances and lowest incorrectly classified instances (95.91% and 4.08% respectively) with least amount of time taken for execution (T = 0.02ms). With respect to the accuracy obtained, it is suggested to use the Win-track platform in hospitals and clinics.
AB - The global prevalence of diabetes mellitus has increased. The use of electronic platform devices has grown in popularity due to their low cost and ease of use. Despite their benefits, however, there remain concerns about their accuracy and precision. The objective of the study is to determine the accuracy and precision of the Win-track platform. In a cross-sectional study, 49 male patients' data were collected. Based on the pressure asserted, the data were further classified into different stages from 1 to 4. The study used four different types of classifiers (Logistic, Multi-layer Perceptron, Simple Logistic Regression and Meta-logit Boost) to check the accuracy. The result shown for all the classifiers was positive with Meta-logit Boost giving the higher Mathews correlation coefficient (MCC) (stage 1=1, stage 2=1, stage 3=0.904 and stage 4=0.912) and highest correctly classified instances and lowest incorrectly classified instances (95.91% and 4.08% respectively) with least amount of time taken for execution (T = 0.02ms). With respect to the accuracy obtained, it is suggested to use the Win-track platform in hospitals and clinics.
UR - http://www.scopus.com/inward/record.url?scp=85145358396&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85145358396&partnerID=8YFLogxK
U2 - 10.1109/DISCOVER55800.2022.9974897
DO - 10.1109/DISCOVER55800.2022.9974897
M3 - Conference contribution
AN - SCOPUS:85145358396
T3 - 2022 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2022 - Proceedings
SP - 282
EP - 286
BT - 2022 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2022 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2022
Y2 - 14 October 2022 through 15 October 2022
ER -
