With　the　development　of　Mobile　Healthcare　Monitoring　Network　(MHMN),　patients＇　data　collected　by　body　sensors　not　only　allows　patients　to　monitor　their　health　or　make　online　pre-diagnosis　but　also　enables　clinicians　to　make　proper　decisions　by　utilizing　data　mining　technique.　However,　sensitive　data　privacy　is　still　a　major　concern.　In　this　article,　we　propose　practical　techniques　for　searching　and　making　online　pre-diagnosis　over　encrypted　data.　First,　we　propose　a　new　Diverse　Keyword　Searchable　Encryption　(DKSE)　scheme　which　supports　multi-dimension　digital　vectors　range　query　and　textual　multi-keyword　ranked　search　to　gain　a　broad　range　of　applications　in　practice.　In　addition,　a　framework　called　PRIDO　based　on　the　DKSE　is　designed　to　protect　patients＇　personal　data　in　data　mining　and　online　pre-diagnosis.　According　to　the　PRIDO　framework,　we　achieve　privacy-preserving　naive　Bayesian　and　decision　tree　classifiers　and　discuss　its　potential　applications　in　actual　deployments.　Security　analysis　proves　that　patients＇　data　privacy　can　be　well　protected　without　loss　of　data　confidentiality,　and　performance　evaluation　demonstrates　the　efficiency　and　accuracy　in　the　diverse　keyword　search,　data　mining,　and　disease　pre-diagnosis,　respectively.