Classification of the severity of diabetic neuropathy: a new approach taking uncertainties into account using fuzzy logic
Clinics
; 67(2): 151-156, 2012. graf, tab
Article
in En
| LILACS
| ID: lil-614639
Responsible library:
BR1.1
ABSTRACT
OBJECTIVE:
This study proposes a new approach that considers uncertainty in predicting and quantifying the presence and severity of diabetic peripheral neuropathy.METHODS:
A rule-based fuzzy expert system was designed by four experts in diabetic neuropathy. The model variables were used to classify neuropathy in diabetic patients, defining it as mild, moderate, or severe. System performance was evaluated by means of the Kappa agreement measure, comparing the results of the model with those generated by the experts in an assessment of 50 patients. Accuracy was evaluated by an ROC curve analysis obtained based on 50 other cases; the results of those clinical assessments were considered to be the gold standard.RESULTS:
According to the Kappa analysis, the model was in moderate agreement with expert opinions. The ROC analysis (evaluation of accuracy) determined an area under the curve equal to 0.91, demonstrating very good consistency in classifying patients with diabetic neuropathy.CONCLUSION:
The model efficiently classified diabetic patients with different degrees of neuropathy severity. In addition, the model provides a way to quantify diabetic neuropathy severity and allows a more accurate patient condition assessment.Key words
Full text:
1
Index:
LILACS
Main subject:
Severity of Illness Index
/
Expert Systems
/
Fuzzy Logic
/
Uncertainty
/
Diabetic Neuropathies
Type of study:
Prognostic_studies
/
Risk_factors_studies
Limits:
Humans
Language:
En
Journal:
Clinics
Journal subject:
MEDICINA
Year:
2012
Type:
Article
/
Project document