Authors : Anbarasi M. S.; Aaron Pushparaj M. M.; Boomika S.; Gowtham

Volume/Issue : Volume 11 - 2026, Issue 4 - April

Google Scholar : https://tinyurl.com/5aazeffd

Scribd : https://tinyurl.com/yntkkevp

DOI : https://doi.org/10.38124/ijisrt/26apr1596

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The growing occurrence of patients with multiple chronic diseases has increased the need for efficient multidisease risk prediction systems in healthcare. However, clinical data is often distributed in various cloud, but it lacks in efficient privacy preservation. To address these challenges, this project proposes a hybridized framework for privacy-aware multi-disease risk prediction in distributed healthcare environments. nA key challenge in multi-disease data collection from heterogeneous clinical data may lag in normalization. So, cleaning and preprocessing is done on the first phase. Second challenge lies in capturing complex and non-linear relationships among clinical features within reduced feature spaces. To handle this, quantum-inspired feature encoding and variational quantum circuits are employed to enhance feature representations. Since data is distributed across cloud data privacy and confidentiality issues exist. To overcome this issue Federated Learning for multi-disease prediction is applied in the third phase. To address these challenges, this paper proposes a novel framework titled Hybridized Quantum Computing and Federated Learning for Multi-Disease Prediction (HQCF-MDRP). The proposed system integrates structured data preprocessing, quantum-assisted feature representation, and federated collaborative learning into a unified architecture. Initially, healthcare datasets are cleaned, normalized, and subjected to feature selection to improve data quality and reduce dimensionality. Selected features are then transformed using quantum-inspired angle encoding and Variational Quantum Circuits (VQC), enabling enhanced representation of hidden correlations among clinical attributes. Subsequently, Federated Learning with FedProx optimization is employed to train predictive models collaboratively across distributed healthcare institutions without sharing raw patient data.

Keywords : Multi-Disease Prediction, Federated Learning, Quantum Computing, Variational Quantum Circuit, Privacy-Preserving Healthcare, Machine Learning, Feature Selection, Distributed Learning, Clinical Data Analytics, Fedprox Optimization.

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