Methods Inf Med - Probability machines: consistent probability estimation using nonparametric learning machines.

Tópicos

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{ estim(2440) model(1874) function(577) }
{ model(3404) distribut(989) bayesian(671) }
{ data(1714) softwar(1251) tool(1186) }
{ perform(999) metric(946) measur(919) }
{ process(1125) use(805) approach(778) }
{ age(1611) year(1155) adult(843) }
{ featur(3375) classif(2383) classifi(1994) }
{ control(1307) perform(991) simul(935) }
{ spatial(1525) area(1432) region(1030) }
{ use(2086) technolog(871) perceiv(783) }
{ imag(1947) propos(1133) code(1026) }
{ imag(2830) propos(1344) filter(1198) }
{ detect(2391) sensit(1101) algorithm(908) }
{ chang(1828) time(1643) increas(1301) }
{ studi(1410) differ(1259) use(1210) }
{ risk(3053) factor(974) diseas(938) }
{ import(1318) role(1303) understand(862) }
{ time(1939) patient(1703) rate(768) }
{ data(1737) use(1416) pattern(1282) }
{ method(1219) similar(1157) match(930) }
{ patient(2315) diseas(1263) diabet(1191) }
{ take(945) account(800) differ(722) }
{ studi(2440) review(1878) systemat(933) }
{ treatment(1704) effect(941) patient(846) }
{ error(1145) method(1030) estim(1020) }
{ research(1085) discuss(1038) issu(1018) }
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{ studi(1119) effect(1106) posit(819) }
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{ can(774) often(719) complex(702) }
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{ system(1976) rule(880) can(841) }
{ measur(2081) correl(1212) valu(896) }
{ imag(1057) registr(996) error(939) }
{ bind(1733) structur(1185) ligand(1036) }
{ sequenc(1873) structur(1644) protein(1328) }
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{ motion(1329) object(1292) video(1091) }
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{ framework(1458) process(801) describ(734) }
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{ featur(1941) imag(1645) propos(1176) }
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{ compound(1573) activ(1297) structur(1058) }
{ blood(1257) pressur(1144) flow(957) }
{ record(1888) medic(1808) patient(1693) }
{ health(3367) inform(1360) care(1135) }
{ monitor(1329) mobil(1314) devic(1160) }
{ state(1844) use(1261) util(961) }
{ patient(2837) hospit(1953) medic(668) }
{ model(2656) set(1616) predict(1553) }
{ data(2317) use(1299) case(1017) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ patient(1821) servic(1111) care(1106) }
{ can(981) present(881) function(850) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
{ use(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
{ result(1111) use(1088) new(759) }
{ implement(1333) system(1263) develop(1122) }
{ decis(3086) make(1611) patient(1517) }
{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }
{ method(2212) result(1239) propos(1039) }

Resumo

CKGROUND: Most machine learning approaches only provide a classification for binary responses. However, probabilities are required for risk estimation using individual patient characteristics. It has been shown recently that every statistical learning machine known to be consistent for a nonparametric regression problem is a probability machine that is provably consistent for this estimation problem.OBJECTIVES: The aim of this paper is to show how random forests and nearest neighbors can be used for consistent estimation of individual probabilities.METHODS: Two random forest algorithms and two nearest neighbor algorithms are described in detail for estimation of individual probabilities. We discuss the consistency of random forests, nearest neighbors and other learning machines in detail. We conduct a simulation study to illustrate the validity of the methods. We exemplify the algorithms by analyzing two well-known data sets on the diagnosis of appendicitis and the diagnosis of diabetes in Pima Indians.RESULTS: Simulations demonstrate the validity of the method. With the real data application, we show the accuracy and practicality of this approach. We provide sample code from R packages in which the probability estimation is already available. This means that all calculations can be performed using existing software.CONCLUSIONS: Random forest algorithms as well as nearest neighbor approaches are valid machine learning methods for estimating individual probabilities for binary responses. Freely available implementations are available in R and may be used for applications.

Resumo Limpo

ckground machin learn approach provid classif binari respons howev probabl requir risk estim use individu patient characterist shown recent everi statist learn machin known consist nonparametr regress problem probabl machin provabl consist estim problemobject aim paper show random forest nearest neighbor can use consist estim individu probabilitiesmethod two random forest algorithm two nearest neighbor algorithm describ detail estim individu probabl discuss consist random forest nearest neighbor learn machin detail conduct simul studi illustr valid method exemplifi algorithm analyz two wellknown data set diagnosi append diagnosi diabet pima indiansresult simul demonstr valid method real data applic show accuraci practic approach provid sampl code r packag probabl estim alreadi avail mean calcul can perform use exist softwareconclus random forest algorithm well nearest neighbor approach valid machin learn method estim individu probabl binari respons freeli avail implement avail r may use applic

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