An Enhanced Model for Smart Healthcare by Integrating Hybrid ML, LSTM, and Blockchain

Abstract

Conventional healthcare systems are traditionally challenged by fragmented data, lack of predictive insights, and security concerns, which spouse their effectiveness and efficiency. This paper will cover these gaps by developing an integrated Smart Healthcare System leveraging the power of the Internet of Things and Artificial Intelligence processes. To that end, we have proposed a holistic model that integrates several advanced methodologies to help in enhanced disease prediction and patient monitoring, with data security and privacy protection. We further apply the Hybrid Machine Learning (ML) models specifically; Random Forest Classifier integrated with k-means clustering for the prediction of diseases. This will cluster patients according to their similarity in health characteristics and provide an accurate disease risk prediction with an accuracy of 85-90%. Accordingly, Long Short Term Memory (LSTM) networks will be used for deeper timestamp series analyses with the following input sets: predicted disease probabilities, time-stamped health monitoring data, and patient lifestyle information sets. This model is outstanding both in regard to forecasting disease progression and in detecting anomalous health events with less than a 5% false positive rate. For protection and integrity of the data, we will use an Ethereum blockchain framework with respective smart contracts. The approach will provide secure, immutable health data storage and controlled, traceable access in full compliance with the requirements of various data protection regulations, such as GDPR. What's more, differentially private computations on encrypted data samples are guaranteed by combining homomorphic encryption methods with differential privacy techniques. The former ensures that in any kind of data analysis, at the point of execution, individual patient privacy is maintained, while the latter ensures an accurate, aggregated health data insight for different scenarios. By incorporating these methods, a robust smart healthcare system would be developed, one which, other than the ability to predict and monitor the progression of a disease very precisely, was able to protect patients' data and respect privacy. The same work has far-reaching implications in achieving better patient outcomes through earlier interventions and provision of increased security to the data, apart from enhancing trust in digital solutions for healthcare.