J Med Syst - Prediction of breast cancer using artificial neural networks.

Tópicos

{ cancer(2502) breast(956) screen(824) }
{ error(1145) method(1030) estim(1020) }
{ time(1939) patient(1703) rate(768) }
{ case(1353) use(1143) diagnosi(1136) }
{ risk(3053) factor(974) diseas(938) }
{ model(2341) predict(2261) use(1141) }
{ model(2656) set(1616) predict(1553) }
{ intervent(3218) particip(2042) group(1664) }
{ method(1219) similar(1157) match(930) }
{ network(2748) neural(1063) input(814) }
{ data(3008) multipl(1320) sourc(1022) }
{ problem(2511) optim(1539) algorithm(950) }
{ concept(1167) ontolog(924) domain(897) }
{ method(984) reconstruct(947) comput(926) }
{ health(1844) social(1437) communiti(874) }
{ decis(3086) make(1611) patient(1517) }
{ can(774) often(719) complex(702) }
{ system(1976) rule(880) can(841) }
{ measur(2081) correl(1212) valu(896) }
{ extract(1171) text(1153) clinic(932) }
{ howev(809) still(633) remain(590) }
{ research(1085) discuss(1038) issu(1018) }
{ system(1050) medic(1026) inform(1018) }
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{ health(3367) inform(1360) care(1135) }
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{ data(1737) use(1416) pattern(1282) }
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{ imag(1057) registr(996) error(939) }
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{ imag(2675) segment(2577) method(1081) }
{ patient(2315) diseas(1263) diabet(1191) }
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{ studi(2440) review(1878) systemat(933) }
{ motion(1329) object(1292) video(1091) }
{ assess(1506) score(1403) qualiti(1306) }
{ treatment(1704) effect(941) patient(846) }
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{ framework(1458) process(801) describ(734) }
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{ import(1318) role(1303) understand(862) }
{ visual(1396) interact(850) tool(830) }
{ compound(1573) activ(1297) structur(1058) }
{ perform(1367) use(1326) method(1137) }
{ studi(1119) effect(1106) posit(819) }
{ spatial(1525) area(1432) region(1030) }
{ record(1888) medic(1808) patient(1693) }
{ model(3480) simul(1196) paramet(876) }
{ monitor(1329) mobil(1314) devic(1160) }
{ ehr(2073) health(1662) electron(1139) }
{ state(1844) use(1261) util(961) }
{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ age(1611) year(1155) adult(843) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ first(2504) two(1366) second(1323) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
{ use(976) code(926) identifi(902) }
{ drug(1928) target(777) effect(648) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }
{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

In this study, an artificial neural network (ANN) was developed to determine whether patients have breast cancer or not. Whether patients have cancer or not and if they have its type can be determined by using ANN and BI-RADS evaluation and based on the age of the patient, mass shape, mass border and mass density. Though this system cannot diagnose cancer conclusively, it helps physicians in deciding whether a biopsy is required by providing information about whether the patient has breast cancer or not. Data obtained from 800 patients who were diagnosed with cancer definitively through biopsy. The definitive diagnosis corresponding to each patient and the data from ANN model results were investigated using Confusion matrix and ROC analyses. In the test data of the ANN model that was implemented as a result of these analyses, disease prediction rate was 90.5% and the health ratio was 80.9%. It is seen from these high predictive values that the ANN model is fast, reliable and without any risks and therefore can be of great help to physicians.

Resumo Limpo

studi artifici neural network ann develop determin whether patient breast cancer whether patient cancer type can determin use ann birad evalu base age patient mass shape mass border mass densiti though system diagnos cancer conclus help physician decid whether biopsi requir provid inform whether patient breast cancer data obtain patient diagnos cancer definit biopsi definit diagnosi correspond patient data ann model result investig use confus matrix roc analys test data ann model implement result analys diseas predict rate health ratio seen high predict valu ann model fast reliabl without risk therefor can great help physician

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