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Document Type

Article

Keywords

Cloud, Machine learning, IoT devices, Healthcare, Data mining

Abstract

Healthcare monitoring Cloud-IoT systems use data mining and machine learning methods to analyse patient data in real-time from linked devices. By offering insights for the early diagnosis of anomalies and individualized treatment suggestions, this strategy improves healthcare management. In this research first the Collect and Load the Clevant Heart Disease Dataset for Data Collection Process. Next, preprocess the loaded data using the Synthetic Minority Oversampling Technique (SMOTE), and then the feature extraction process is done using the Principal Component Analysis (PCA) Method. In this case, the characteristic must be extracted by feeding a specific column. The classification procedure is then carried out using Generative Adversarial Networks (GAN) and an optimization approach called Adaptive Moment Estimation. This is where the model executes GAN operations, and the output will be produced. The data is then transferred to an edge-cloud environment to minimize storage problems and provide instant access to critical data. This process starts with the encryption and decryption of data using Homomorphic encryption with the Laplacian technique. In addition, have taken the generated values from the GAN network as original values and encrypt them using Homomorphic encryption with the Laplacian technique. Next, the routing process is done using the leach protocol to optimize energy consumption and communication efficiency. The leach protocol is used to route among the data to divide the data into clusters and perform energy consumption. Finally, the simulation of this research is conducted by Python – 3.9.6 network simulator, and the performance of the proposed model is estimated based on various performance metrics such as accuracy at 90%, precision at 94%, authentication time, throughput at 90%, and packet delivery ratio with 94% this demonstrated that the suggested effort produced better results both in terms of quantitative and qualitative aspects.

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