A Novel Krill Herd Based Random Forest Algorithm for Monitoring Patient Health  

作　　者：Md.Moddassir Alam Md Mottahir Alam Muhammad Moinuddin Mohammad Tauheed Ahmad Jabir Hakami Anis Ahmad Chaudhary Asif Irshad Khan Tauheed Khan Mohd 

机构地区：[1]Department of Health Information Management and Technology,University of Hafr Al-Batin,Saudi Arabia [2]Department of Electrical and Computer Engineering,Faculty of Engineering,King Abdulaziz,Jeddah,21589,Saudi Arabia [3]Center of Excellence in Intelligent Engineering Systems,King Abdul Aziz University,Jeddah,Saudi Arabia [4]College of Medicine,King Khalid University,Abha,Saudi Arabia [5]Department of Physics,College of Science,Jazan University,P.O.Box.114,Jazan,45142,Saudi Arabia [6]Department of Biology,College of Science,Imam Mohammad Ibn Saud Islamic University(IMSIU),Riyadh,11623,Saudi Arabia [7]Computer Science Department,Faculty of Computing and Information Technology,King Abdulaziz University,Jeddah,21589,Saudi Arabia [8]Department of Math and Computer Science,Augustana College,IL,61201,United States

出　　处：《Computers, Materials & Continua》2023年第5期4553-4571,共19页计算机、材料和连续体（英文）

基　　金：The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Small Research Groups under grant number(RGP.1/62/43).

摘　　要：Artificial Intelligence(AI)is finding increasing application in healthcare monitoring.Machine learning systems are utilized for monitoring patient health through the use of IoT sensor,which keep track of the physiological state by way of various health data.Thus,early detection of any disease or derangement can aid doctors in saving patients’lives.However,there are some challenges associated with predicting health status using the common algorithms,such as time requirements,chances of errors,and improper classification.We propose an Artificial Krill Herd based on the Random Forest(AKHRF)technique for monitoring patients’health and eliciting an optimal prescription based on their health status.To begin with,various patient datasets were collected and trained into the system using IoT sensors.As a result,the framework developed includes four processes:preprocessing,feature extraction,classification,and result visibility.Additionally,preprocessing removes errors,noise,and missing values from the dataset,whereas feature extraction extracts the relevant information.Then,in the classification layer,we updated the fitness function of the krill herd to classify the patient’s health status and also generate a prescription.We found that the results fromthe proposed framework are comparable to the results from other state-of-the-art techniques in terms of sensitivity,specificity,Area under the Curve(AUC),accuracy,precision,recall,and F-measure.

关 键 词：Healthcare system health monitoring clinical decision support internet of things artificial intelligence machine learning diagnosis 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]