TY - JOUR
KW - Data Mining
KW - Case-Based Reasoning
KW - Classification
KW - Case Retrieval
KW - Diabetes Application
KW - Fuzzy Decision Tree
KW - Fuzzy Rule Base
KW - Rule Induction
AU - Baghdad Atmani
AU - Mohammed Benamina
AU - Sofia Benbelkacem
AB - In the medical field, experts’ knowledge is based on experience, theoretical knowledge and rules. Case-based reasoning is a problem-solving paradigm which is based on past experiences. For this purpose, a large number of decision support applications based on CBR have been developed. Cases retrieval is often considered as the most important step of case-based reasoning. In this article, we integrate fuzzy logic and data mining to improve the response time and the accuracy of the retrieval of similar cases. The proposed Fuzzy CBR is composed of two complementary parts; the part of classification by fuzzy decision tree realized by Fispro and the part of case-based reasoning realized by the platform JColibri. The use of fuzzy logic aims to reduce the complexity of calculating the degree of similarity that can exist between diabetic patients who require different monitoring plans. The results of the proposed approach are compared with earlier methods using accuracy as metrics. The experimental results indicate that the fuzzy decision tree is very effective in improving the accuracy for diabetes classification and hence improving the retrieval step of CBR reasoning.
IS - Regular Issue
M1 - 3
N2 - In the medical field, experts’ knowledge is based on experience, theoretical knowledge and rules. Case-based reasoning is a problem-solving paradigm which is based on past experiences. For this purpose, a large number of decision support applications based on CBR have been developed. Cases retrieval is often considered as the most important step of case-based reasoning. In this article, we integrate fuzzy logic and data mining to improve the response time and the accuracy of the retrieval of similar cases. The proposed Fuzzy CBR is composed of two complementary parts; the part of classification by fuzzy decision tree realized by Fispro and the part of case-based reasoning realized by the platform JColibri. The use of fuzzy logic aims to reduce the complexity of calculating the degree of similarity that can exist between diabetic patients who require different monitoring plans. The results of the proposed approach are compared with earlier methods using accuracy as metrics. The experimental results indicate that the fuzzy decision tree is very effective in improving the accuracy for diabetes classification and hence improving the retrieval step of CBR reasoning.
PY - 2018
SP - 72
EP - 80
T2 - International Journal of Interactive Multimedia and Artificial Intelligence
TI - Diabetes Diagnosis by Case-Based Reasoning and Fuzzy Logic
UR - http://www.ijimai.org/journal/sites/default/files/files/2018/02/ijimai_5_3_8_pdf_20127.pdf
VL - 5
SN - 1989-1660
ER -