In　cloud-based　health　monitoring　services,　healthcare　centers　often　outsource　support　vector　machine　(SVM)-based　clinical　decision　models　to　provide　remote　users　with　clinical　decisions.　During　service　provisioning,　authorized　external　organizations　like　insurance　companies　aim　to　verify　decision　correctness　to　prevent　fraudulent　medical　reimbursements.　However,　existing　verifiable　and　secure　SVM　classification　schemes　have　predominantly　focused　on　user　self-verification,　thereby　introducing　potential　risks　of　privacy　leakage　(such　as　input　data　exposure)　in　publicly　verifiable　scenarios.　To　address　the　aforementioned　limitation,　we　propose　a　publicly　verifiable　and　secure　SVM　classification　scheme　(PVSSVM)　for　cloud-based　health　monitoring　services　in　a　malicious　setting,　which　can　accommodate　the　verification　needs　of　users　or　authorized　external　organizations　with　respect　to　potential　malicious　results　returned　by　cloud　servers.　Specifically,　we　utilize　homomorphic　encryption　and　secret　sharing　to　protect　the　model　and　data　confidentiality　in　the　cloud　server,　respectively.　Based　on　a　multiserver　verifiable　computation　framework,　PVSSVM　achieves　public　verification　of　predicted　results.　Additionally,　we　further　investigate　its　performance.　Experimental　evaluations　demonstrate　that　PVSSVM　outperforms　existing　state-of-the-art　solutions　in　terms　of　computation　and　communication　overhead.　Notably,　in　the　verification　scenario　of　large-scale　predictions,　the　proposed　scheme　achieves　a　reduction　of　approximately　83.71%　in　computation　overhead　through　batch　verification,　as　compared　to　one-by-one　verification.