深度梯度提升模型及其在腦卒中預(yù)測(cè)中的應(yīng)用

深度梯度提升模型及其在腦卒中預(yù)測(cè)中的應(yīng)用摘要

隨著數(shù)據(jù)量的增加，提升算法已經(jīng)成為了機(jī)器學(xué)習(xí)中的一大研究熱點(diǎn)。其中，梯度提升模型(GradientBoostingMachine,GBM)是一種常見(jiàn)的提升算法。但是，標(biāo)準(zhǔn)的GBM對(duì)于深度神經(jīng)網(wǎng)絡(luò)的非線性建模能力較弱，為了克服這一缺陷，研究人員發(fā)展出了深度梯度提升模型（DeepGradientBoostingMachine,DGBM）。

本研究主要使用深度梯度提升模型，對(duì)腦卒中的預(yù)測(cè)建模。通過(guò)仿真實(shí)驗(yàn)的結(jié)果表明，與傳統(tǒng)提升算法和神經(jīng)網(wǎng)絡(luò)算法相比，DGBM具有更好的預(yù)測(cè)性能，可以更好地發(fā)現(xiàn)腦卒中的相關(guān)風(fēng)險(xiǎn)因素。

通過(guò)實(shí)驗(yàn)，我們發(fā)現(xiàn)DGBM具有較強(qiáng)的自適應(yīng)擬合能力，可以自動(dòng)學(xué)習(xí)輸入變量的重要性，提升數(shù)據(jù)的處理效率。此外，相對(duì)于傳統(tǒng)的機(jī)器學(xué)習(xí)算法，DGBM對(duì)數(shù)據(jù)的連續(xù)性要求較低，具有更廣泛的應(yīng)用范圍。

關(guān)鍵詞：深度梯度提升模型；腦卒中預(yù)測(cè)；自適應(yīng)擬合；風(fēng)險(xiǎn)因素；數(shù)據(jù)處理

Abstract

Withtheincreaseofdatavolume,boostingalgorithmshavebecomeahotresearchtopicinmachinelearning.Amongthem,GradientBoostingMachine(GBM)isacommonboostingalgorithm.However,thestandardGBMhasweaknonlinearmodelingabilityfordeepneuralnetworks.Toovercomethisshortcoming,researchershavedevelopedDeepGradientBoostingMachine(DGBM).

Inthisstudy,wemainlyusetheDeepGradientBoostingMachinetomodelthepredictionofstroke.Theresultsofsimulationexperimentsshowthatcomparedwithtraditionalboostingalgorithmsandneuralnetworkalgorithms,DGBMhasbetterpredictionperformanceandcanbetterdiscoverrelevantriskfactorsforstroke.

Throughexperiments,wefoundthatDGBMhasstrongadaptivefittingability,canautomaticallylearntheimportanceofinputvariables,andimprovetheefficiencyofdataprocessing.Inaddition,comparedwithtraditionalmachinelearningalgorithms,DGBMhasalowerrequirementfordatacontinuityandhasawiderrangeofapplications.

Keywords:DeepGradientBoostingMachine;StrokePrediction;AdaptiveFitting;RiskFactors;DataProcessingStrokeisamajorhealthconcernandaleadingcauseofdisabilityanddeathglobally.Riskfactorsforstrokecanbebroadlygroupedintotwocategories,modifiableandnon-modifiable.Non-modifiableriskfactorsincludeage,gender,genetics,andfamilyhistory.However,modifiableriskfactorsplayasignificantroleinthepreventionofstroke.

Themodifiableriskfactorsforstrokeincludehighbloodpressure,smoking,diabetes,highcholesterol,obesity,physicalinactivity,unhealthydiet,andexcessivealcoholconsumption.Theseriskfactorsincreasethelikelihoodofdevelopingatherosclerosis,whichisthehardeningandnarrowingofthearteries,andcanleadtostroke.

Highbloodpressureisthemostimportantmodifiableriskfactorforstroke.Itdamagesthebloodvessels,makingthemmorepronetoblockageorrupture,leadingtostroke.Smokingincreasestheriskofstrokebydamagingthebloodvesselsandincreasingtheformationofbloodclots.Diabetesincreasestheriskofstrokebydamagingthebloodvesselsandincreasingthelikelihoodofbloodclotsformation.

Highcholesterollevelscontributetotheformationofatherosclerosisandincreasestheriskofstroke.Obesity,physicalinactivity,andunhealthydietscontributetothedevelopmentofatherosclerosisandincreasetheriskofstroke.Excessivealcoholconsumptioncontributestohighbloodpressureandincreasestheriskofstroke.

Inconclusion,identifyingmodifiableriskfactorsforstrokeiscriticalinthepreventionandmanagementofstroke.TheDeepGradientBoostingMachine(DGBM)hasemergedasapowerfultoolinpredictingtheriskofstrokeandidentifyingrelevantriskfactors.Itsadaptivefittingability,automaticlearningofinputvariableimportance,andwiderangeofapplicationsmakeitaneffectiveoptionfordataprocessinginstrokepredictionFurthermore,besidesthetraditionalriskfactorsforstrokesuchashypertension,diabetes,andsmoking,emergingriskfactorssuchasairpollutionandsleepapneahavegainedattentioninrecentyears.Airpollutionhasbeenfoundtoincreasetheriskofstrokebypromotinginflammationandoxidativestress,whilesleepapnea,acommonbreathingdisorderduringsleep,isassociatedwithanincreasedriskofstrokeduetodisruptedoxygensupplytothebrain.

Itisalsoworthnotingthatstrokepreventionandmanagementrequireamultidisciplinaryapproachinvolvingnotonlymedicalprofessionalsbutalsopatients,families,andcommunities.Patienteducationandlifestylemodificationprograms,suchasregularexercise,healthyeating,andstressreduction,cancomplementmedicaltreatmentsandreducetheriskofstroke.

Inconclusion,strokeisamajorpublichealthissueworldwide,withahighburdenofmortalityanddisability.Whilethetraditionalriskfactorsforstrokeremainsignificant,newriskfactorshaveemerged,anddataprocessingtoolssuchasDGBMcanaidintheiridentificationandmanagement.Collaborativeeffortsamonghealthcareprofessionals,patients,families,andcommunitiesarenecessarytopreventandmanagestrokeeffectivelyStrokeisacomplexandheterogeneousdiseasewithahighburdenofmortalityanddisability.Itrequiresamultidisciplinaryapproachtoitsmanagement,includingprimaryprevention,acutetreatment,andrehabilitation.Whiletherehavebeensignificantadvancementsinstrokeresearchandtreatment,thereisstillmuchworktobedonetoeffectivelypreventandmanagestroke.

Oneareaofresearchthatshowspromiseisthedevelopmentofnovelbiomarkersforstroke.Biomarkersaremeasurableindicatorsofabiologicalstateorprocessandcanprovidevaluableinformationontheunderlyingmechanismsofstroke.Forexample,certainproteinsinthebloodorcerebrospinalfluidmayindicateinflammation,oxidativestress,orvascularinjury,allofwhichareknowntocontributetostrokedevelopmentandprogression.

Anotherareaofresearchthatholdspromiseistheuseoftelestrokeandtelemedicinetechnologiestoimprovestrokecareinunderservedorremoteareas.Telestrokeinvolvestheuseofvideoconferencingandtelecommunicationstechnologiestoconnectstrokespecialistswithpatientsandhealthcareprovidersinremotelocations.Thisenablestimelydiagnosis,treatment,andtransferofpatients,improvingoutcomesandreducingtheburdenonlocalhealthcarefacilities.

Finally,strokepreventionremainsacrucialaspectofstrokemanagement.Whiletraditionalriskfactorssuchashypertension,diabetes,andsmokingremainsignificant,newriskfactorssuchasairpollutionandpoorsleepqualityhaveemer