Applications of machine learning and deep learning in healthcare: Breast cancer case
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Date
2025-05-25
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Publisher
Université Mohamed Boudiaf - M’sila
Abstract
Breast 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.
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Keywords
Breast cancer · Data balancing · Deep neuralnetwork · Feature selection · Hyperparameter optimization · Machine learning · Prediction