Turner　syndrome　adheres　serious　health-related　complications　with　a　tendency　to　affect　various　organs　during　different　stages　of　life　which　includes　hypertension,　infertility,　and　retarded　growth.　The　proper　diagnosis　of　TS　requires　an　expensive　test　named　karyotype　test　which　is　not　easily　available　in　remote　health　care　units　in　the　countryside.　Therefore,　we　proposed　to　use　facial　images　to　detect　TS　to　pursue　a　higher　accuracy　of　recognition.　The　proposed　scheme　achieved　the　accuracy　of　91.3%　with　mixed　feature　extraction　schemes　using　thirty　principle　components　selected　with　criteria　that　retained　95%　of　the　information　from　the　turner　dataset.　Moreover,　this　research　is　the　first　that　uses　facial　features　to　accurately　diagnose　TS　patients　and　has　the　capability　to　help　doctors　to　establish　a　cost-effective　TS　prognosis　process　in　remote　health　care　units　that　lack　required　health　care　facilities.　©　2020,　Springer　Nature　Singapore　Pte　Ltd.