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.

Colon cancer is a growth of cells that begins in a part of the large intestine called the colon. Colon cancer happens when cells in the colon develop changes in their DNA. Consequently, fewer infections and fatalities may result from early identification of this cancer. Histological analysis is used for a final diagnosis of colon cancer. Histopathology, or the microscopic examination of damaged tissue, is crucial for both cancer diagnosis and treatment. This work suggests a novel deep learning technique for colon cancer detection effectively. Histopathology images are collected from various type of sources. To enhance the quality of raw images, pre-processed techniques such as image scaling, colour map improved image sharpening, and image restoration are used. Resize the image's dimensions in image resizing to minimize the processing time. A colour map enhances the sharpness of an image by combining two techniques: The contrast adjustment technique is used to alter the image's contrast first. The resultant image is then enhanced by applying the image sharpening process and scaling it using a weighting fraction. As using the final image has increased quality, blur and undesirable noise are removed using image restoration. Next, the pre-data are used in the Attention U-Net segmentation procedure, which segments the region of the pre-data. To extract features from this segmented image to perform an accurate diagnosis, efficientnetB0 is used. In data extraction, the Bidirectional GRU model is used to process the data further in order to develop predictions. When processing input sequences in both directions with the BiGRU model, it is feasible to gather contextual information to increase accuracy and predict colon cancer effectively. In the proposed model colon disease prediction classifier offer 97% accuracy, 96% specificity and 95.49% F1_score. Thus, the proposed model effectively predicts colon cancer and improves accuracy.