Major　depressive　disorder　(MDD)　is　an　issue　that　affects　350　million　people　worldwide.　Traditional　approaches　have　been　to　identify　depressive　symptoms　in　datasets,　but　recently,　research　is　beginning　to　explore　the　association　between　psychosocial　factors　such　as　those　on　the　quality　of　life　scale　and　mental　well-being,　which　will　lead　to　earlier　diagnosis　and　prediction　of　MDD.　In　this　research,　an　ensemble　binary　classifier　is　proposed　to　analyse　health　survey　data　against　ground　truth　from　the　SF-20　Quality　of　Life　scales.　The　classifier　aims　to　improve　the　performance　of　machine　learning　techniques　on　large　datasets　and　identify　depressed　cases　based　on　associations　between　items　on　the　QoL　scale　and　mental　illness　by　increasing　predictive　performance.　On　the　experimental　evaluation　on　the　National　Health　and　Nutrition　Examination　Survey　(NHANES),　the　classifier　demonstrated　an　F1　score　of　0.976　in　the　prediction,　without　any　incorrectly　identified　depression　instances.　Only　about　4%　of　instances　had　been　mistakenly　classified　into　depressed　cases,　with　a　significant　accuracy　of　95.4%　comparing　to　the　result　from　PHQ-9　mental　screen　inventory.　The　presented　ensemble　binary　classifier　performed　comparably　better　than　each　baseline　algorithm　in　all　measures　and　all　experiments.　We　trained　the　ensemble　model　on　the　processed　NHANES　dataset,　tested　and　evaluated　the　results　of　its　performance　against　mental　screen　inventory　and　discussed　the　comparable　predictions.　Finally,　we　provided　future　research　directions.