Discovering human understandable fuzzy diagnostic rules from medical data
نویسندگان
چکیده
The paper describes an approach to discover transparent fuzzy rules from data, which can be effectively used in fuzzy model-based medical diagnosis. The approach is based on three main stages. First, available symptoms measurements are clustered by our Crisp Double Clustering scheme, which identifies, in the first instance, informative prototypes in the original measurements space by a vector quantization algorithm. Then, these prototypes are clustered on each one-dimensional projection to provide information for the second stage, where fuzzy information granules are properly quantified in terms of fuzzy sets that are immediate to read and understand. Finally, the derived fuzzy sets are employed to define a rule-based fuzzy inference system that can be used for fuzzy diagnosis. The approach has been applied to the Aachen Aphasia dataset.
منابع مشابه
Discovering interpretable classification rules from neural processed data
In this paper we describe a neuro-fuzzy model to extract interpretable classification rules from examples. Such model is trained in a parameter subspace where a number of formal properties, which characterize understandable knowledge bases, are satisfied. To deal with the curse of dimensionality problem, which occurs when our model is used in high-dimensional classification tasks, an "A Priori ...
متن کاملA neuro-fuzzy network to generate human-understandable knowledge from data
Neuro-fuzzy networks have been successfully applied to extract knowledge from data in the form of fuzzy rules. However, one drawback with the neuro-fuzzy approach is that the fuzzy rules induced by the learning process are not necessarily understandable. The lack of readability is essentially due to the high dimensionality of the parameter space that leads to excessive flexibility in the modifi...
متن کاملPartially supervised learning of fuzzy classification rules
The research area of Data Mining or Knowledge Discovery in Databases has emerged in response to the challenges of analyzing the tremendously growing datasets gathered nowadays by companies and research institutions. Classification is one important task of data mining, where fuzzy techniques to extract classification rules from data are appealing due to their human understandable modeling. Often...
متن کاملA Survey of Fuzzy Based Association Rule Mining to Find Co- Occurrence Relationships
Data mining is the analysis step of the "Knowledge Discovery in Databases" process, or KDD. It is the process that results in the discovery of new patterns in large data sets. It utilizes methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract knowledge from an existing data set and tra...
متن کاملMining association rules with improved semantics in medical databases
The discovery of new knowledge by mining medical databases is crucial in order to make an effective use of stored data, enhancing patient management tasks. One of the main objectives of data mining methods is to provide a clear and understandable description of patterns held in data. We introduce a new approach to find association rules among quantitative values in relational databases. The sem...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2003