IEEE J Biomed Health Inform - Rule extraction from support vector machines using ensemble learning approach: an application for diagnosis of diabetes.

Tópicos

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{ system(1976) rule(880) can(841) }
{ patient(2315) diseas(1263) diabet(1191) }
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{ import(1318) role(1303) understand(862) }
{ decis(3086) make(1611) patient(1517) }
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{ extract(1171) text(1153) clinic(932) }
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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) }
{ group(2977) signific(1463) compar(1072) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
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{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Diabetes mellitus is a chronic disease and a worldwide public health challenge. It has been shown that 50-80% proportion of T2DM is undiagnosed. In this paper, support vector machines are utilized to screen diabetes, and an ensemble learning module is added, which turns the "black box" of SVM decisions into comprehensible and transparent rules, and it is also useful for solving imbalance problem. Results on China Health and Nutrition Survey data show that the proposed ensemble learning method generates rule sets with weighted average precision 94.2% and weighted average recall 93.9% for all classes. Furthermore, the hybrid system can provide a tool for diagnosis of diabetes, and it supports a second opinion for lay users.

Resumo Limpo

diabet mellitus chronic diseas worldwid public health challeng shown proport tdm undiagnos paper support vector machin util screen diabet ensembl learn modul ad turn black box svm decis comprehens transpar rule also use solv imbal problem result china health nutrit survey data show propos ensembl learn method generat rule set weight averag precis weight averag recal class furthermor hybrid system can provid tool diagnosi diabet support second opinion lay user

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