Hybrid optimization based learning technique for multi‐disease analytics from healthcare big data using optimal pre‐processing, clustering and classifier

Abstract

With the rapid development of health information systems in recent years covering big data trends, clinical information is being adapted to understand important health trends and provide timely prophylactic treatment. However, the current plan fails to maintain a highly organized analysis and processing of health data. In addition, it collects issues that play an important role in determining quality. In this article, we propose a hybrid optimization based learning technique for multi‐disease analytics (HOL‐MD) from healthcare big data using optimal pre‐processing, clustering, and classifier. First, we introduce a capuchin search based optimization algorithm for preprocessing which removes the unwanted artifacts to enhance the detection accuracy. Second, we introduce a modified Harris Hawks optimization based clustering (MHHOC) technique used to select optimal features among multiple features which discovers the subgroups and reduce the dimensionality issues. We evaluated the performance of proposed HOL‐MD technique using standard US healthcare organization dataset, SUSY and HIGGS datasets. Finally, we proved the effectiveness of proposed RTL‐DNN techniques in terms of 11.5%, 9.3%, 2.75%, and 3.45% higher than the existing state‐of‐art techniques in terms of accuracy, precision, recall, and F‐measure respectively.

Investigating the dimensions, components, and key indicators of the use of big data in the health industry

“…Remote monitoring, patient profile analysis and genomic analysis also influence the decision-making process. This feature makes the decision-making process faster, simpler and more accurate (Parimanam et al, 2022).  Cost reduction: Big Data helps reduce medical costs by anticipating and diagnosing the disease early, making smart decisions, implementing cost-effective treatments, monitoring adherence to treatment, and reducing transportation costs.…”

“… Personalized medical upgrades: Access and processing of large volumes of data enables the provision of personalized services to the patient.  Globalization: With the globalization of data, access to more information and service providers can share their information and knowledge and be informed and benefit from new knowledge and methods (Park et al, 2022).  Creating Advantages for Health Payers and Detecting Fraud: Health insurance companies are subject to changing laws.…”

Accelerated Driving-Training-Based Optimization for Solving Constrained Bi-Objective Stochastic Optimization Problems

“…In the dynamic healthcare landscape, where patient care, technological advancements, and operational excellence intersect, effective resource allocation is critical. Optimization of resource allocation can ensure efficient utilization of staff, time, and budget while maintaining high-quality patient care [23][24][25]. The number of emergency cases has increased quickly each year, resulting in long-term overcrowding in emergency departments.…”