Comput Methods Programs Biomed - An attribute weight assignment and particle swarm optimization algorithm for medical database classifications.

Tópicos

{ learn(2355) train(1041) set(1003) }
{ model(2656) set(1616) predict(1553) }
{ method(1969) cluster(1462) data(1082) }
{ featur(3375) classif(2383) classifi(1994) }
{ case(1353) use(1143) diagnosi(1136) }
{ system(1050) medic(1026) inform(1018) }
{ cancer(2502) breast(956) screen(824) }
{ problem(2511) optim(1539) algorithm(950) }
{ cost(1906) reduc(1198) effect(832) }
{ system(1976) rule(880) can(841) }
{ imag(2675) segment(2577) method(1081) }
{ take(945) account(800) differ(722) }
{ gene(2352) biolog(1181) express(1162) }
{ activ(1452) weight(1219) physic(1104) }
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{ treatment(1704) effect(941) patient(846) }
{ clinic(1479) use(1117) guidelin(835) }
{ method(1557) propos(1049) approach(1037) }
{ method(984) reconstruct(947) comput(926) }
{ data(3963) clinic(1234) research(1004) }
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{ health(3367) inform(1360) care(1135) }
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{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ data(3008) multipl(1320) sourc(1022) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
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{ health(1844) social(1437) communiti(874) }
{ high(1669) rate(1365) level(1280) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
{ result(1111) use(1088) new(759) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ process(1125) use(805) approach(778) }

Resumo

In this research, a hybrid model is developed by integrating a case-based reasoning approach and a particle swarm optimization model for medical data classification. Two data sets from UCI Machine Learning Repository, i.e., Liver Disorders Data Set and Breast Cancer Wisconsin (Diagnosis), are employed for benchmark test. Initially a case-based reasoning method is applied to preprocess the data set thus a weight vector for each feature is derived. A particle swarm optimization model is then applied to construct a decision-making system for diseases identified. The PSO algorithm starts by partitioning the data set into a relatively large number of clusters to reduce the effects of initial conditions and then reducing the number of clusters into two. The average forecasting accuracy for breast cancer of CBRPSO model is 97.4% and for liver disorders is 76.8%. The proposed case-based particle swarm optimization model is able to produce more accurate and comprehensible results for medical experts in medical diagnosis.

Resumo Limpo

research hybrid model develop integr casebas reason approach particl swarm optim model medic data classif two data set uci machin learn repositori ie liver disord data set breast cancer wisconsin diagnosi employ benchmark test initi casebas reason method appli preprocess data set thus weight vector featur deriv particl swarm optim model appli construct decisionmak system diseas identifi pso algorithm start partit data set relat larg number cluster reduc effect initi condit reduc number cluster two averag forecast accuraci breast cancer cbrpso model liver disord propos casebas particl swarm optim model abl produc accur comprehens result medic expert medic diagnosi

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