Electronic Medical Records (EMRs) are computerized copies of paper records used in healthcare settings. Data from these systems allows doctors to quickly access and manage patients' medical records at healthcare institutions. Medical data security safeguards patients' rights and the duties of healthcare workers. Sharing such medical records with another medical organization is challenging for them. Cloud computing (CC) is the most effective way for storing this sort of data and addressing these issues. In this research a novel DOuble SEcure MEDical Cloud data (DOSE MED) technique has been proposed that enhances privacy and security of medical data. The proposed DOSE MED method consists of three stages namely, registration phase, encryption phase and storage/decision phase. The proposed method utilizes Okamoto Uchiyama's homomorphic encryption technique to add prediction capability on the cloud which paves the way for disease prediction by medical practitioners. Euclidean distance-based classifier has been used for predicting data without decrypting it. The security findings demonstrate that the proposed DOSE MED provides selective security for the chosen keyword assaults. Performance analysis and real-world simulation trials demonstrate that this system is both efficient and practicable