BEGHRICHE Tawfiq2025-05-252025-05-252025-05-25EL/DOC1354/2025https://repository.univ-msila.dz/handle/123456789/46221Breast cancerrankssecondinfatalityamongwomen.Conventionaldiagnosticmethodsaretime-consuming,exhausting,and expensive,potentiallyleadingtodelayedtreatmentormisdiagnosis.Machinelearning(ML)anddeeplearning(DL)methods haveshownoutstandingpotential.However,theyfacechallengeslikefeatureidentificationandselection,dataimbalance, and parameteroptimization.Unlikemostknownsolutionsthathaveexploredthesestagesforbreastcancerprediction,either separately orinlimitedcombinations,ourapproachsimultaneouslytacklesthesecriticalissuesusingmulti-stageoptimization architecture. Threewell-establishedtechniques,namelycorrelationanalysis-basedfeatureselection(CFS),LASSOregres- sion, andmutualinformation(MI),areusedforFS.Databalancingisperformedusingbothoversamplingandundersampling techniques, includingthesyntheticminorityoversamplingtechnique(SMOTE),k-nearestneighboroversampling(KNNOR), and randomundersampling(RUS).Finally,hyperparameteroptimization(HPO)iscarriedoutbyadoptingvariousmethods including gridsearch,randomsearch,Bayesianoptimization,andsemi-automatictomaximizetheclassificationperfor- mance ofsevenrenownedMLalgorithms(logisticregression,decisiontree,randomforest,supportvectormachine,Naïve Bayes, k-nearestneighbor,andeXtremegradientboosting),andadeepneuralnetwork(DNN).Throughtheexperiments carried outonfourpubliclyavailabledatasets,includingWisconsindiagnosticbreastcancer(WDBC),Wisconsinbreast cancer dataset(WBCD),Wisconsinprognosticbreastcancer(WPBC),andBreastcancercoimbra(BCC),theobtainedresults clearly demonstratethesuperiorityoftheproposedmethodoverthestate-of-the-artmethods.frBreast cancer · Data balancing · Deep neuralnetwork · Feature selection · Hyperparameter optimization · Machine learning · PredictionThesis