Classification of the severity of diabetic neuropathy: a new approach taking uncertainties into account using fuzzy logic
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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier España
2012-01-01
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| Series: | Clinics |
| Subjects: | |
| Online Access: | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1807-59322012000200010 |
| _version_ | 1811319801563316224 |
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| author | Andreja P. Picon Neli R. S. Ortega Ricky Watari Cristina Sartor Isabel C. N. Sacco |
| author_facet | Andreja P. Picon Neli R. S. Ortega Ricky Watari Cristina Sartor Isabel C. N. Sacco |
| author_sort | Andreja P. Picon |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T12:48:50Z |
| format | Article |
| id | doaj.art-ecd362eacfce454891d8b5a8a54164bc |
| institution | Directory Open Access Journal |
| issn | 1807-5932 1980-5322 |
| language | English |
| last_indexed | 2024-04-13T12:48:50Z |
| publishDate | 2012-01-01 |
| publisher | Elsevier España |
| record_format | Article |
| series | Clinics |
| spelling | doaj.art-ecd362eacfce454891d8b5a8a54164bc2022-12-22T02:46:18ZengElsevier EspañaClinics1807-59321980-53222012-01-01672151156DOI:10.6061/clinics/2012(02)10Classification of the severity of diabetic neuropathy: a new approach taking uncertainties into account using fuzzy logicAndreja P. PiconNeli R. S. OrtegaRicky WatariCristina SartorIsabel C. N. SaccoOBJECTIVE: 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.http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1807-59322012000200010Diabetic NeuropathiesFuzzy setsDiabetes mellitusExpert systems |
| spellingShingle | Andreja P. Picon Neli R. S. Ortega Ricky Watari Cristina Sartor Isabel C. N. Sacco Classification of the severity of diabetic neuropathy: a new approach taking uncertainties into account using fuzzy logic Clinics Diabetic Neuropathies Fuzzy sets Diabetes mellitus Expert systems |
| title | Classification of the severity of diabetic neuropathy: a new approach taking uncertainties into account using fuzzy logic |
| title_full | Classification of the severity of diabetic neuropathy: a new approach taking uncertainties into account using fuzzy logic |
| title_fullStr | Classification of the severity of diabetic neuropathy: a new approach taking uncertainties into account using fuzzy logic |
| title_full_unstemmed | Classification of the severity of diabetic neuropathy: a new approach taking uncertainties into account using fuzzy logic |
| title_short | Classification of the severity of diabetic neuropathy: a new approach taking uncertainties into account using fuzzy logic |
| title_sort | classification of the severity of diabetic neuropathy a new approach taking uncertainties into account using fuzzy logic |
| topic | Diabetic Neuropathies Fuzzy sets Diabetes mellitus Expert systems |
| url | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1807-59322012000200010 |
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