Enhancing Healthcare Data Security and Disease Detection Using Crossover-Based Multilayer Perceptron in Smart Healthcare Systems
作者机构：Department of Industrial EngineeringCollege of EngineeringKing Saud UniversityP.O.Box-800Riyadh11421Saudi Arabia
出 版 物：《Computer Modeling in Engineering & Sciences》 (工程与科学中的计算机建模（英文）)
年 卷 期：2024年第139卷第4期
页 面：977-997页
核心收录：
学科分类：0839[工学-网络空间安全] 08[工学] 081201[工学-计算机系统结构] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：funded by King Saud University through Researchers Supporting Program Number (RSP2024R499)
主 题：Smart healthcare systems multilayer perceptron cybersecurity adversarial attack detection Healthcare 4.0
摘 要：The healthcare data requires accurate disease detection analysis,real-timemonitoring,and advancements to ensure proper treatment for ***,Machine Learning methods are widely utilized in Smart Healthcare Systems(SHS)to extract valuable features fromheterogeneous and high-dimensional healthcare data for predicting various diseases and monitoring patient *** methods are employed across different domains that are susceptible to adversarial attacks,necessitating careful ***,this paper proposes a crossover-based Multilayer Perceptron(CMLP)*** collected samples are pre-processed and fed into the crossover-based multilayer perceptron neural network to detect adversarial attacks on themedical records of *** an attack is detected,healthcare professionals are promptly alerted to prevent data *** paper utilizes two datasets,namely the synthetic dataset and the University of Queensland Vital Signs(UQVS)dataset,from which numerous samples are *** results are conducted to evaluate the performance of the proposed CMLP model,utilizing various performancemeasures such as Recall,Precision,Accuracy,and F1-score to predict patient *** the proposed method with existing approaches,it achieves the highest accuracy,precision,recall,and ***,the proposedmethod achieves a precision of 93%,an accuracy of 97%,an F1-score of 92%,and a recall of 92%.
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