Efficacy of Fuzzy-Stat Modelling in Classification of Gynaecologists and Patients
Fuzzy logic-based inference systems depend on the domain experts’ perceptions, which are intrinsically imprecise/vague/fuzzy. The perceptions of more than one expert are needed in the decision-making process. Therefore, there is a need to study the similarity between the experts using a mathematical...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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De Gruyter
2016-04-01
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| Series: | Journal of Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/jisys-2015-0001 |
| _version_ | 1818733811862601728 |
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| author | Sardesai Anjali Kharat Vilas Sambarey Pradip Deshpande Ashok |
| author_facet | Sardesai Anjali Kharat Vilas Sambarey Pradip Deshpande Ashok |
| author_sort | Sardesai Anjali |
| collection | DOAJ |
| description | Fuzzy logic-based inference systems depend on the domain experts’ perceptions, which are intrinsically imprecise/vague/fuzzy. The perceptions of more than one expert are needed in the decision-making process. Therefore, there is a need to study the similarity between the experts using a mathematical framework. Classical mathematical models simulating the medical diagnostic process are usually either logical or probabilistic, wherein the concept of partial belief is not considered. Except in a few cases, binary logic is too unrealistic to apply to medical diagnosis. Another important factor in medical science is the patient-symptom relationship, which influences the disease diagnosis. In summary, the following two issues stand out: (i) Do experts agree with one another in arriving at the same diagnostic labels? (ii) Based on the symptom-patient relationship, can patients be classified? The authors have tried to explore the possibility of using fuzzy similarity measures and also Gower’s coefficient in classifying gynaecologists and patients. The comparative evaluation infers that the efficacy of two-valued binary logic-based Gower’s coefficient is low. |
| first_indexed | 2024-12-17T23:55:24Z |
| format | Article |
| id | doaj.art-4439f58f465348a1a355ff622da5e066 |
| institution | Directory Open Access Journal |
| issn | 0334-1860 2191-026X |
| language | English |
| last_indexed | 2024-12-17T23:55:24Z |
| publishDate | 2016-04-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Journal of Intelligent Systems |
| spelling | doaj.art-4439f58f465348a1a355ff622da5e0662022-12-21T21:28:05ZengDe GruyterJournal of Intelligent Systems0334-18602191-026X2016-04-0125214715710.1515/jisys-2015-0001Efficacy of Fuzzy-Stat Modelling in Classification of Gynaecologists and PatientsSardesai Anjali0Kharat Vilas1Sambarey Pradip2Deshpande Ashok3Department of Computer Science, Savitribai Phule Pune University, Ganeshkhind, Pune 411 007, IndiaDepartment of Computer Science, Savitribai Phule Pune University, Ganeshkhind, Pune 411 007, IndiaDepartment of Gynecology, Swami Ramanand Teerth Rural Medical College, Ambajogai 431 517, IndiaBerkeley Initiative in Soft Computing (BISC), Special Interest Group (SIG), Environment Management Systems (EMS), University of California, 94720-2284 Berkeley, CA, USA; and Row House, Sandhya Nagari, Pune-Wakad Road, Pune 411 027, India, Tel.: +9120-7275307/+917588871607Fuzzy logic-based inference systems depend on the domain experts’ perceptions, which are intrinsically imprecise/vague/fuzzy. The perceptions of more than one expert are needed in the decision-making process. Therefore, there is a need to study the similarity between the experts using a mathematical framework. Classical mathematical models simulating the medical diagnostic process are usually either logical or probabilistic, wherein the concept of partial belief is not considered. Except in a few cases, binary logic is too unrealistic to apply to medical diagnosis. Another important factor in medical science is the patient-symptom relationship, which influences the disease diagnosis. In summary, the following two issues stand out: (i) Do experts agree with one another in arriving at the same diagnostic labels? (ii) Based on the symptom-patient relationship, can patients be classified? The authors have tried to explore the possibility of using fuzzy similarity measures and also Gower’s coefficient in classifying gynaecologists and patients. The comparative evaluation infers that the efficacy of two-valued binary logic-based Gower’s coefficient is low.https://doi.org/10.1515/jisys-2015-0001medical diagnosissimilarity measurescosine amplitude methodfuzzy set theorygower’s coefficientmax-min method |
| spellingShingle | Sardesai Anjali Kharat Vilas Sambarey Pradip Deshpande Ashok Efficacy of Fuzzy-Stat Modelling in Classification of Gynaecologists and Patients Journal of Intelligent Systems medical diagnosis similarity measures cosine amplitude method fuzzy set theory gower’s coefficient max-min method |
| title | Efficacy of Fuzzy-Stat Modelling in Classification of Gynaecologists and Patients |
| title_full | Efficacy of Fuzzy-Stat Modelling in Classification of Gynaecologists and Patients |
| title_fullStr | Efficacy of Fuzzy-Stat Modelling in Classification of Gynaecologists and Patients |
| title_full_unstemmed | Efficacy of Fuzzy-Stat Modelling in Classification of Gynaecologists and Patients |
| title_short | Efficacy of Fuzzy-Stat Modelling in Classification of Gynaecologists and Patients |
| title_sort | efficacy of fuzzy stat modelling in classification of gynaecologists and patients |
| topic | medical diagnosis similarity measures cosine amplitude method fuzzy set theory gower’s coefficient max-min method |
| url | https://doi.org/10.1515/jisys-2015-0001 |
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