A blockchain-enabled healthcare system for cervical cancer risk prediction using enhanced metaheuristic optimised graph convolutional attention based GRU

Abstract

Cervical cancer is a serious health concern that entails high risks for individuals due to delayed detection and treatment worldwide. Formal screening for the condition is challenging in developing countries due to several factors, including medical costs, access to healthcare facilities, and delayed symptom manifestation. A blockchain-enabled healthcare system for cervical cancer risk prediction ensures data security, privacy, and accurate risk assessment. This system uses blockchain to provide decentralised, tamper-proof storage and access control over sensitive patient data, ensuring that only authorized entities can interact with the information. An improved spotted hyena optimization algorithm is employed for cervical cancer risk prediction, fine-tuning a Graph Convolutional Network (GCN) integrated with an Attention Mechanism and a Gated Recurrent Unit (GRU). The GCN captures complex relationships between medical attributes and patients, while the attention mechanism dynamically assigns weights to features based on relevance, improving predictive accuracy. The GRU processes sequential data, such as medical history, to model temporal dependencies in the risk factors. The metaheuristic optimization further enhances the model by finding the optimal parameters, boosting performance

Introduces a blockchain-enabled system for secure and decentralized medical data management

Applies an intelligent model for predicting cervical cancer risk using patient health records

Demonstrates improved accuracy, privacy, and reliability over traditional diagnostic methods