AI-Driven Architecture for Diabetic Disease Prediction using IoT Monitoring and Blockchain-Secured LSTM Networks

Abstract

In today's healthcare system, data overload presents challenges and opportunities. This effort uses Blockchain and Machine Learning to recommend new healthcare data management methods. Machine learning rapidly extracts meaningful data from enormous datasets. Blockchain technology also employs consensus mechanisms to protect healthcare data, making data exchange more reliable. Blockchain, deep learning, and IoT improve healthcare. Technology's rapid expansion allows this integration to provide real-time monitoring, secure data management, and accurate disease prediction. IoT-connected medical devices record patient health data for early diagnosis and treatment. However, handling and preserving such large amounts of sensitive medical data remains a challenge. Blockchain technology secures, privates, and interoperates healthcare providers' data by offering a distributed, immutable, and tamper-proof ledger. Time-series health data is evaluated using recurrent neural networks (RNNs), notably LSTM networks, to improve predictive analytics. LSTM can detect long-term dependencies in sequential data, making it good at forecasting diabetic problems. Through these technologies, we can develop a trustworthy, effective, and smart healthcare environment that allows for earlier diagnosis, more targeted treatment regimens, and improved patient outcomes. This platform automates data processing and prediction, relieving clinicians of some of their workload while improving healthcare security and efficiency. These benefits won't guarantee widespread adoption unless computing costs, scalability, and integration with healthcare infrastructures are addressed. Future research should improve these technologies to make healthcare safer, easier, and patient-centered.

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