Internet of Things (IoT) has revolutionized mobile healthcare applications, increases diagnosis speed and accuracy. Disease prediction systems (DPS) improve healthcare quality, but they raise privacy concerns due to sensitive data. These concerns include illegal sharing, misuse, and exposure of sensitive information. However, developing new techniques does not provide improved protection from attackers and fraudsters. This paper suggests an effective privacy-preserving strategy for patient healthcare data from IoT devices in order to anticipate diseases in the contemporary medical field. Heart failure prediction health data is utilized as an input in this proposed approach. Initially, elastic net (EN) is used to reduce the dimensionality of the input raw dataset. Hyper parameter in EN is optimally selected using the Zebra Optimization Algorithm (ZOA). After dimensionality reduction, data is encrypted using the ElGamal technique. During the encryption procedure, the True Random Number Generator-Pseudo Random Number Generator (TRNG-PRNG) encryption method is used to generate the secret key. These encrypted data is securely stored in cloud. Finally, Attention Mechanism based Bi-directional Gated Recurrent Unit (AT-BiGRU) technique is employed to predict heart disease. The ElGamal-TRPRNG method strengths privacy and security by achieving encryption and decryption times of 0.30 sec and 0.12 sec, respectively. The suggested model is evaluated and contrasted with current methods using encryption data performance measures. Model achieved 93.47% accuracy, 6.53% error, and 93.45% precision in encrypted data performance metrics. Therefore, this suggested method is the most effective way to effectively safeguard health care records.