J Med Syst - Neural network diagnostic system for dengue patients risk classification.

Tópicos

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{ take(945) account(800) differ(722) }
{ age(1611) year(1155) adult(843) }
{ patient(2315) diseas(1263) diabet(1191) }
{ case(1353) use(1143) diagnosi(1136) }
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{ treatment(1704) effect(941) patient(846) }
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{ health(3367) inform(1360) care(1135) }
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{ state(1844) use(1261) util(961) }
{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ data(2317) use(1299) case(1017) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
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{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
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{ activ(1138) subject(705) human(624) }
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{ analysi(2126) use(1163) compon(1037) }
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{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
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Resumo

With the dramatic increase of the worldwide threat of dengue disease, it has been very crucial to correctly diagnose the dengue patients in order to decrease the disease severity. However, it has been a great challenge for the physicians to identify the level of risk in dengue patients due to overlapping of the medical classification criteria. Therefore, this study aims to construct a noninvasive diagnostic system to assist the physicians for classifying the risk in dengue patients. Systematic producers have been followed to develop the system. Firstly, the assessment of the significant predictors associated with the level of risk in dengue patients was carried out utilizing the statistical analyses technique. Secondly, Multilayer perceptron neural network models trained via Levenberg-Marquardt and Scaled Conjugate Gradient algorithms was employed for constructing the diagnostic system. Finally, precise tuning for the models' parameters was conducted in order to achieve the optimal performance. As a result, 9 noninvasive predictors were found to be significantly associated with the level of risk in dengue patients. By employing those predictors, 75% prediction accuracy has been achieved for classifying the risk in dengue patients using Scaled Conjugate Gradient algorithm while 70.7% prediction accuracy were achieved by using Levenberg-Marquardt algorithm.

Resumo Limpo

dramat increas worldwid threat dengu diseas crucial correct diagnos dengu patient order decreas diseas sever howev great challeng physician identifi level risk dengu patient due overlap medic classif criteria therefor studi aim construct noninvas diagnost system assist physician classifi risk dengu patient systemat produc follow develop system first assess signific predictor associ level risk dengu patient carri util statist analys techniqu second multilay perceptron neural network model train via levenbergmarquardt scale conjug gradient algorithm employ construct diagnost system final precis tune model paramet conduct order achiev optim perform result noninvas predictor found signific associ level risk dengu patient employ predictor predict accuraci achiev classifi risk dengu patient use scale conjug gradient algorithm predict accuraci achiev use levenbergmarquardt algorithm

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