Matches in SemOpenAlex for { <https://semopenalex.org/work/W2308984926> ?p ?o ?g. }
Showing items 1 to 74 of
74
with 100 items per page.
- W2308984926 endingPage "9" @default.
- W2308984926 startingPage "1" @default.
- W2308984926 abstract "Technology can be defined as an instrument which allows improved understanding medical data and better management of their health records. Rapidly changing medical technology and changing practice pattern of physicians have revolutionized health care monitoring. Today's medical research could be more advanced, more effective for the society by the application of computer algorithms in large medical data analysis. There is an ever increasing demand for technology based diagnostic predictions to anticipate and prevent complications of major diseases like diabetes, cancer, hypertension, and heart and liver disorders. Clustering is one of the data mining techniques for analyzing such medical datasets. It is a technique for finding similarity groups in data, called clusters. It groups data instances that are similar to each other in one cluster from the data instances that are very different from each other into a different cluster. Clustering is classified as an unsupervised learning task. The paper identifies different symptoms in different types of diabetic patients which are the clusters to be grouped. Similarity measure technique isolates the probable disease group for a particular patient. Fuzzy clustering extends this notion to associate each pattern with every cluster using a membership function. The paper finally predicts the most probable type of diabetes pertaining to a patient using the Minkowski metric of the unsupervised algorithm from the cluster of assumed symptom levels over a range. The same technique can be applied to predict any type of disease given the symptoms." @default.
- W2308984926 created "2016-06-24" @default.
- W2308984926 creator A5000433815 @default.
- W2308984926 date "2015-01-01" @default.
- W2308984926 modified "2023-09-22" @default.
- W2308984926 title "Fuzzy cluster analysis using unsupervised algorithm for the diagnosis of types of diabetes" @default.
- W2308984926 cites W2140190241 @default.
- W2308984926 hasPublicationYear "2015" @default.
- W2308984926 type Work @default.
- W2308984926 sameAs 2308984926 @default.
- W2308984926 citedByCount "0" @default.
- W2308984926 crossrefType "journal-article" @default.
- W2308984926 hasAuthorship W2308984926A5000433815 @default.
- W2308984926 hasConcept C103278499 @default.
- W2308984926 hasConcept C115961682 @default.
- W2308984926 hasConcept C124101348 @default.
- W2308984926 hasConcept C142724271 @default.
- W2308984926 hasConcept C154945302 @default.
- W2308984926 hasConcept C164866538 @default.
- W2308984926 hasConcept C17212007 @default.
- W2308984926 hasConcept C199360897 @default.
- W2308984926 hasConcept C41008148 @default.
- W2308984926 hasConcept C534262118 @default.
- W2308984926 hasConcept C58166 @default.
- W2308984926 hasConcept C63085389 @default.
- W2308984926 hasConcept C71924100 @default.
- W2308984926 hasConcept C73555534 @default.
- W2308984926 hasConcept C8038995 @default.
- W2308984926 hasConceptScore W2308984926C103278499 @default.
- W2308984926 hasConceptScore W2308984926C115961682 @default.
- W2308984926 hasConceptScore W2308984926C124101348 @default.
- W2308984926 hasConceptScore W2308984926C142724271 @default.
- W2308984926 hasConceptScore W2308984926C154945302 @default.
- W2308984926 hasConceptScore W2308984926C164866538 @default.
- W2308984926 hasConceptScore W2308984926C17212007 @default.
- W2308984926 hasConceptScore W2308984926C199360897 @default.
- W2308984926 hasConceptScore W2308984926C41008148 @default.
- W2308984926 hasConceptScore W2308984926C534262118 @default.
- W2308984926 hasConceptScore W2308984926C58166 @default.
- W2308984926 hasConceptScore W2308984926C63085389 @default.
- W2308984926 hasConceptScore W2308984926C71924100 @default.
- W2308984926 hasConceptScore W2308984926C73555534 @default.
- W2308984926 hasConceptScore W2308984926C8038995 @default.
- W2308984926 hasIssue "3" @default.
- W2308984926 hasLocation W23089849261 @default.
- W2308984926 hasOpenAccess W2308984926 @default.
- W2308984926 hasPrimaryLocation W23089849261 @default.
- W2308984926 hasRelatedWork W2067498424 @default.
- W2308984926 hasRelatedWork W2078824636 @default.
- W2308984926 hasRelatedWork W2144593802 @default.
- W2308984926 hasRelatedWork W2293874129 @default.
- W2308984926 hasRelatedWork W2612292012 @default.
- W2308984926 hasRelatedWork W2619678004 @default.
- W2308984926 hasRelatedWork W2767822119 @default.
- W2308984926 hasRelatedWork W2783045248 @default.
- W2308984926 hasRelatedWork W2793921726 @default.
- W2308984926 hasRelatedWork W2887872040 @default.
- W2308984926 hasRelatedWork W2899091260 @default.
- W2308984926 hasRelatedWork W2910976128 @default.
- W2308984926 hasRelatedWork W2912644745 @default.
- W2308984926 hasRelatedWork W2921774528 @default.
- W2308984926 hasRelatedWork W2922441119 @default.
- W2308984926 hasRelatedWork W2924662495 @default.
- W2308984926 hasRelatedWork W2963989903 @default.
- W2308984926 hasRelatedWork W2969651194 @default.
- W2308984926 hasRelatedWork W2993291313 @default.
- W2308984926 hasRelatedWork W3022458966 @default.
- W2308984926 hasVolume "3" @default.
- W2308984926 isParatext "false" @default.
- W2308984926 isRetracted "false" @default.
- W2308984926 magId "2308984926" @default.
- W2308984926 workType "article" @default.