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

Computers, Materials & Continua 2023年第5期75卷 4553-4571页

作者： Md.Moddassir Alam Md Mottahir Alam Muhammad Moinuddin Mohammad tauheed Ahmad Jabir Hakami Anis Ahmad Chaudhary Asif Irshad khan tauheed khan mohd Department of Health Information Management and Technology University of Hafr Al-BatinSaudi Arabia Department of Electrical and Computer Engineering Faculty of EngineeringKing AbdulazizJeddah21589Saudi Arabia Center of Excellence in Intelligent Engineering Systems King Abdul Aziz UniversityJeddahSaudi Arabia College of Medicine King Khalid UniversityAbhaSaudi Arabia Department of Physics College of ScienceJazan UniversityP.O.Box.114Jazan45142Saudi Arabia Department of Biology College of ScienceImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11623Saudi Arabia Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Math and Computer Science Augustana CollegeIL61201United States

Artificial Intelligence(AI)is finding increasing application in healthcare *** 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 ***,early detection of any disease or derangement can aid doctors in saving patients’***,there are some challenges associated with predicting health status using the common algorithms,such as time requirements,chances of errors,and improper *** 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 *** begin with,various patient datasets were collected and trained into the system using IoT *** a result,the framework developed includes four processes:preprocessing,feature extraction,classification,and result ***,preprocessing removes errors,noise,and missing values from the dataset,whereas feature extraction extracts the relevant ***,in the classification layer,we updated the fitness function of the krill herd to classify the patient’s health status and also generate a *** 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

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Attention-Based Deep Learning Model for Early Detection of Parkinson’s Disease

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Computers, Materials & Continua 2022年第6期71卷 5183-5200页

作者： mohd Sadiq mohd tauheed khan Sarfaraz Masood Department of Computer Engineering Jamia Millia IslamiaNew Delhi-110025India Department of Math and Computer Science Augustana CollegeRock IslandIllinois-61201USA

Parkinson’s disease(PD),classified under the category of a neurological syndrome,affects the brain of a person which leads to the motor and non-motor *** motor symptoms,one of the major disabling symptom is Freezing of Gait(FoG)that affects the daily standard of living of PD *** treatments target to improve the symptoms of *** of PD at the early stages is an arduous task due to being indistinguishable from a healthy *** work proposed a novel attention-basedmodel for the detection of FoG events and PD,andmeasuring the intensity of PD on the United Parkinson’s Disease Rating *** separate datasets,that is,UCF Daphnet dataset for detection of Freezing of Gait Events and PhysioNet Gait in PD Dataset were used for training and validating on their respective *** results show a definite rise in the various performance metrics when compared to landmark models on these problems using these *** results strongly suggest that the proposed state of the art attention-based deep learning model provide a consistent as well as an efficient solution to the selected *** valueswere obtained for various performance metrics like accuracy of 98.74%for detection FoG,98.72%for detection of PD and 98.05%for measuring the intensity of PD on *** model was also analyzed for robustness against noisy samples,where also model exhibited consistent *** results strongly suggest that the proposed model provides a better classification method for selected problem.

关键词： Parkinson’s disease freezing of gait the attention mechanism hyperparameter tuning attentive-FoGPDNet

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