Problems in establishing the medical expert systems CADIAG-1 and CADIAG-2 in rheumatology
Journal of Medical Systems
Representation and semiautomatic acquisition of medical knowledge in CADIAG-1 and CADIAG-2
Computers and Biomedical Research
Performance evaluation of medical expert systems using ROC curves
Computers and Biomedical Research
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Biomedical Informatics: Computer Applications in Health Care and Biomedicine (Health Informatics)
Biomedical Informatics: Computer Applications in Health Care and Biomedicine (Health Informatics)
Multiclass Support Vector Machines for EEG-Signals Classification
IEEE Transactions on Information Technology in Biomedicine
Evaluation of Fuzzy Relation Method for Medical Decision Support
Journal of Medical Systems
Modeling Paradigms for Medical Diagnostic Decision Support: A Survey and Future Directions
Journal of Medical Systems
| Hi-index | 0.00 |
This paper investigates a variation to Adlassnig's fuzzy relation based model for medical diagnosis. The proposed model is an attempt to closely replicate a physician's perceptions of symptom-disease associations and his approximate-reasoning for diagnosis. For proof of principle, the algorithm is evaluated in two sample studies. First case study relates to selected cardiovascular diseases, wherein the required parameters are estimated by interviewing physicians, and an evaluation is performed on a dataset of 79 cases. In the second study, the algorithm is implemented using an alternative semiautomatic approach for a more complex problem of diagnosing common infectious diseases, wherein the parameters are derived from a dataset of 92 case records; for evaluation, jack-knife is performed along with a comparison with Independence Bayes, considered here as the reference standard. The proposed algorithm was found to be as accurate as Independence Bayes for diagnosing common infectious diseases from the small dataset. This result may indicate the utility of proposed algorithm to optimally model the diagnostic process for small datasets; especially, due to its computational simplicity. Further studies on a variety of datasets are needed to establish such a utility.