J Med Syst - A new data preparation method based on clustering algorithms for diagnosis systems of heart and diabetes diseases.

Tópicos

{ learn(2355) train(1041) set(1003) }
{ featur(3375) classif(2383) classifi(1994) }
{ model(2656) set(1616) predict(1553) }
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{ cancer(2502) breast(956) screen(824) }
{ implement(1333) system(1263) develop(1122) }
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{ data(1737) use(1416) pattern(1282) }
{ method(1219) similar(1157) match(930) }
{ patient(2315) diseas(1263) diabet(1191) }
{ error(1145) method(1030) estim(1020) }
{ blood(1257) pressur(1144) flow(957) }
{ can(981) present(881) function(850) }
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{ data(3963) clinic(1234) research(1004) }
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{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
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{ detect(2391) sensit(1101) algorithm(908) }

Resumo

The most important factors that prevent pattern recognition from functioning rapidly and effectively are the noisy and inconsistent data in databases. This article presents a new data preparation method based on clustering algorithms for diagnosis of heart and diabetes diseases. In this method, a new modified K-means Algorithm is used for clustering based data preparation system for the elimination of noisy and inconsistent data and Support Vector Machines is used for classification. This newly developed approach was tested in the diagnosis of heart diseases and diabetes, which are prevalent within society and figure among the leading causes of death. The data sets used in the diagnosis of these diseases are the Statlog (Heart), the SPECT images and the Pima Indians Diabetes data sets obtained from the UCI database. The proposed system achieved 97.87 %, 98.18 %, 96.71 % classification success rates from these data sets. Classification accuracies for these data sets were obtained through using 10-fold cross-validation method. According to the results, the proposed method of performance is highly successful compared to other results attained, and seems very promising for pattern recognition applications.

Resumo Limpo

import factor prevent pattern recognit function rapid effect noisi inconsist data databas articl present new data prepar method base cluster algorithm diagnosi heart diabet diseas method new modifi kmean algorithm use cluster base data prepar system elimin noisi inconsist data support vector machin use classif newli develop approach test diagnosi heart diseas diabet preval within societi figur among lead caus death data set use diagnosi diseas statlog heart spect imag pima indian diabet data set obtain uci databas propos system achiev classif success rate data set classif accuraci data set obtain use fold crossvalid method accord result propos method perform high success compar result attain seem promis pattern recognit applic

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