Machine Learning
Integrating Rule-Based and Case-Based Decision Making in Diabetic Patient Management
ICCBR '99 Proceedings of the Third International Conference on Case-Based Reasoning and Development
A Model for Centering Visual Stimuli Through Adaptive Value Learning
IWANN '93 Proceedings of the International Workshop on Artificial Neural Networks: New Trends in Neural Computation
A new hybrid case-based architecture for medical diagnosis
Information Sciences—Informatics and Computer Science: An International Journal
Global optimization of case-based reasoning for breast cytology diagnosis
Expert Systems with Applications: An International Journal
Temporal data mining for the quality assessment of hemodialysis services
Artificial Intelligence in Medicine
Expert Systems with Applications: An International Journal
A predictive model for cerebrovascular disease using data mining
Expert Systems with Applications: An International Journal
Association rule discovery with the train and test approach for heart disease prediction
IEEE Transactions on Information Technology in Biomedicine
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Retinopathy or blindness due to diabetes is one of the most common complications among diabetics worldwide. Due to its high prevalence, early detections are necessary so as to avoid vision loss. This paper aims to discuss the design and development of a retinopathy predictive system which is based on data mining and case based reasoning CBR. To be specific, C5.0 was used to produce the decision tree whereas k-nearest neighbour and Hamming distance algorithms were used to select the three most similar cases for every new case entered into the system. Then a voting mechanism makes the final prediction. Results show that the hybrid system has a better accuracy prediction rate 85% compared to C5.0 76% and CBR 73% implemented solely.