In recent years, health monitoring systems have become popular due to increasing medical costs and advances in wireless technology. Organisations use information systems to develop and distribute information as well as gather, filter, and process data. Patients are responsible for sharing information with particular types of healthcare providers in order to make an accurate diagnosis and decide on the most appropriate model of treatment. In health monitoring systems, hospitals outsource clinical decision-making models to cloud service providers, who then use the models to make clinical decisions based on the biomedical information they receive from remote clients. Biomedical data as well as clinical decision-making models must be protected since privacy issues are extremely serious. An effective categorization technique for health monitoring systems using a privacy-preserving decision tree (PPDT) is given in this analysis. Decision tree classification with privacy preservation is carried out by using the encrypted token to search the encrypted index. According to a performance analysis, PPDT is extremely effective in terms of accuracy, time, storage, and communication.