Hybridized Quantum Computing and Federated Learning for Multi-Disease Prediction
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…
Read the paper
Related studies
- In silico toxicology protocols
- QM7-X, a comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules
- Quantum Informed Machine-Learning Potentials for Molecular Dynamics Simulations of CO2's Chemisorption and Diffusion in Mg-MOF-74
- In-silico strategies in nano-drug design: Bridging nanomaterials and pharmacological applications
- mRNA codon optimization with quantum computers
- The computational power of the human brain
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.