Background:　Ovarian　cancer　is　a　difficult　and　lethal　illness　that　requires　early　detection　and　precise　classification　for　effective　therapy.　Microarray　technology　has　permitted　the　simultaneous　assessment　of　hundreds　of　genes＇　expression　levels,　yielding　important　insights　into　the　molecular　pathways　driving　ovarian　cancer.　To　reduce　computational　complexity　and　improve　accuracy,　choosing　the　most　likely　differential　genes　to　explain　the　impacts　of　ovarian　cancer　is　necessary.　Medical　datasets,　including　those　related　to　ovarian　cancer,　are　often　limited　in　size　due　to　privacy　concerns,　data　collection　challenges,　and　the　rarity　of　certain　conditions.　Data　augmentation　allows　researchers　to　expand　the　dataset,　providing　a　larger　and　more　diverse　set　of　examples　for　model　training.　Recent　advances　in　machine　learning　and　bioinformatics　have　shown　promise　in　improving　ovarian　cancer　classification　based　on　gene　information.　Methods:　In　this　paper,　we　present　an　ensemble　algorithm　based　on　gene　selection,　data　augmentation,　and　boosting　approaches　for　ovarian　cancer　classification.　In　the　proposed　approach,　the　initial　genetic　data　were　first　subjected　to　feature　selection.　Results:　The　target　genes　were　screened　and　combined　with　data　augmentation　and　ensemble　boosting　algorithms.　From　the　results,　the　chosen　ten　genes　could　accurately　classify　ovarian　cancer　at　98.21%.　Conclusions:　We　further　show　that　the　proposed　algorithm　based　on　clustering　approaches　is　effective　for　real-world　ovarian　cancer　data,　with　100%　accuracy　and　strong　performance　in　distinguishing　between　distinct　ovarian　cancer　subtypes.　The　proposed　algorithm　may　help　doctors　identify　ovarian　cancer　patients　early　and　develop　individualized　treatment　plans.