Medical Practitioner-Centric Heterogeneous Network Powered Efficient E-Healthcare Risk Prediction on Health Big Data

• DOI: 10.1142/s0218843024500126
• Publication Date: 2024-01-18T14:18:06Z
• Authors: P. Sathyaprakash; Poovendran Alagar...; Mohanarangan Veer...; Ahmed Alkhayyat; Parthasarathy Poo...; Deevi Radha Rani; V. Savitha
• Keywords: 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology

From a Licensed Medical Practitioner’s (LMP) perspective, e-Healthcare Risk Prediction plays vital role in Health Big Data. This also is hot issue e-healthcare because of the lack security and privacy protections. To overcome this deficiency, research article proposes heterogeneous network systems (HNS), an efficient privacy-preserving method for e-healthcare. In comparison to existing contribution, proposed HNS accomplish two steps disease risk prediction, namely Analysis HNS, Heterogeneous Network (HetNet) concerning LMP analyzing in-hospital involvement care by collecting explaining “Health Data” as per view LMP. will help access services from hospital. LMP-Centric Powered Efficient phase, “Polygenic Score” calculated prediction health big data. Through characteristics “non-predictive applications” “Predictive applications,” procedural aspects are analyzed with HetNet against Prediction. be applied extensive data integration clustering handling Finally, Data treats perspective efficiently. The system increased accuracy 45.9%, monogenic score 3% 19%. density range 13.9% 39%. execution time improved 29.95% 36.05%. comprehensive analysis 73.98% efficient.