Citation :

Tamilselvan Kaliyaperumal, Poonguzhali Ramaiyan, "Unified Multi-Disease Prediction and Risk Assessment Framework: Hybrid Deep Learning with Secure Multi-Party Computation for Comorbidity Analysis," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 8, pp. 571-583, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I8P138

Abstract

Modern healthcare systems are experiencing severe problems in both forecasting comorbid conditions and patient privacy amid decentralized healthcare facilities. This paper introduces a single framework in which multi-task deep learning and secure multi-party computation are combined to result in privacy-preserving disease prediction and risk assessment. The suggested Multi-Task Modified Deep Neural Network (MT-MDLNN) system with five conditions proposed simultaneously to determine cardiovascular disease, diabetes mellitus, chronic kidney disease, hypertension, respiratory disorders and bi- or inter-disease associations. A new Correlation Aware Hybrid Whale-Coati Optimization (CA-HWCO) algorithm explicitly captures comorbidity patterns and it attains better convergence and clinically significant disease relationship learning. To achieve collaborative training between healthcare institutions without sharing sensitive patient information, the framework uses secure multi-party computation protocols that are based on Modified Elliptic Curve Diffie-Hellman (M-ECDH) cryptography as well as secret sharing schemes. It is experimentally validated on six publicly available healthcare datasets with extraordinarily high mean classification accuracy (92.4) and can recognize pattern of clinically validated comorbidity such as diabetes-kidney disease correlation (0.76), as well as cardiovascular-hypertension patterns (0.79). Secure aggregation protocols add as little as 6-9% overhead to communication but information theorems not only in order distance but also in image value fidelity. Risk stratification analysis depicts that 89.4% are in accord with expert clinical examination with real-time inference ability of 38ms per patient. This study provides an overall remedy towards privacy-sensitive collaborative healthcare intelligence by allowing medical institutions to come up with precise multi-disease prediction models without failure to the privacy of patients.

Keywords

Comorbidity prediction, M-ECDH cryptography, Multi-task learning, Privacy-preserving healthcare AI, Secure Multi-Party Computation.

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