Pub Date : 2024-05-21DOI: 10.3991/ijoe.v20i08.47765
abdlhakim aouragh, Mohamed Bahaj, Fouad Toufik
Diabetes, a pervasive global health concern, presents diagnostic challenges due to its nuanced onset and far-reaching implications. Traditional diagnostic approaches, reliant on time-consuming assessments, necessitate a paradigm shift towards more efficient methodologies. In response, this study introduces a diagnostic support system leveraging the power of optimized machine learning algorithms. Addressing class imbalance within a dataset comprising 768 records, our methodology intricately weaves together feature selection, dimensionality reduction techniques, and grid search optimization. Specifically, the Extra Trees model, fine-tuned via grid search, emerges as the most potent, showcasing remarkable performance metrics: an accuracy score of 92.5%, an F1-score of 93.7%, and an AUC-ROC of 92.47%. These findings underscore the pivotal role of machine learning in reshaping diabetes diagnosis, offering transformative possibilities for global healthcare enhancement.
糖尿病是全球普遍关注的健康问题，由于其细微的发病原因和深远的影响，给诊断带来了挑战。传统的诊断方法依赖于耗时的评估，因此有必要转变模式，采用更高效的方法。为此，本研究利用优化机器学习算法的强大功能，引入了一种诊断支持系统。针对由 768 条记录组成的数据集中的类不平衡问题，我们的方法将特征选择、降维技术和网格搜索优化巧妙地结合在一起。具体来说，通过网格搜索进行微调的 Extra Trees 模型是最有效的模型，其性能指标非常出色：准确率为 92.5%，F1 分数为 93.7%，AUC-ROC 为 92.47%。这些发现强调了机器学习在重塑糖尿病诊断中的关键作用，为全球医疗保健的提升提供了变革性的可能性。
{"title":"Diabetes Prediction: Optimization of Machine Learning through Feature Selection and Dimensionality Reduction","authors":"abdlhakim aouragh, Mohamed Bahaj, Fouad Toufik","doi":"10.3991/ijoe.v20i08.47765","DOIUrl":"https://doi.org/10.3991/ijoe.v20i08.47765","url":null,"abstract":"Diabetes, a pervasive global health concern, presents diagnostic challenges due to its nuanced onset and far-reaching implications. Traditional diagnostic approaches, reliant on time-consuming assessments, necessitate a paradigm shift towards more efficient methodologies. In response, this study introduces a diagnostic support system leveraging the power of optimized machine learning algorithms. Addressing class imbalance within a dataset comprising 768 records, our methodology intricately weaves together feature selection, dimensionality reduction techniques, and grid search optimization. Specifically, the Extra Trees model, fine-tuned via grid search, emerges as the most potent, showcasing remarkable performance metrics: an accuracy score of 92.5%, an F1-score of 93.7%, and an AUC-ROC of 92.47%. These findings underscore the pivotal role of machine learning in reshaping diabetes diagnosis, offering transformative possibilities for global healthcare enhancement.","PeriodicalId":507997,"journal":{"name":"International Journal of Online and Biomedical Engineering (iJOE)","volume":"135 17","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141114857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-21DOI: 10.3991/ijoe.v20i08.48421
Edward Jordy Ticlavilca-Inche, Maria Isabel Moreno-Lozano, Pedro Castañeda, Sandra Wong-Durand, Alejandra Oñate-Andino
This article introduces an innovative mobile solution for Pterygium detection, an eye disease, using a classification model based on the convolutional neural network (CNN) architecture ResNext50 in images of the anterior segment of the eye. Four models (ResNext50, ResNet50, MobileNet v2, and DenseNet201) were used for the analysis, with ResNext50 standing out for its high accuracy and diagnostic efficiency. The research, focused on applications for ophthalmological medical centers in Lima, Peru, explains the process of development and integration of the ResNext50 model into a mobile application. The results indicate the high effectiveness of the system, highlighting its high precision, recall, and specificity, which exceed 85%, thus showing its potential as an advanced diagnostic tool in ophthalmology. This system represents a significant tool in ophthalmology, especially for areas with limited access to specialists, offering a rapid and reliable diagnosis of Pterygium. The study also addresses the technical challenges and clinical implications of implementing this technology in a real-world context.
{"title":"Mobile Application Based on Convolutional Neural Networks for Pterygium Detection in Anterior Segment Eye Images at Ophthalmological Medical Centers","authors":"Edward Jordy Ticlavilca-Inche, Maria Isabel Moreno-Lozano, Pedro Castañeda, Sandra Wong-Durand, Alejandra Oñate-Andino","doi":"10.3991/ijoe.v20i08.48421","DOIUrl":"https://doi.org/10.3991/ijoe.v20i08.48421","url":null,"abstract":"This article introduces an innovative mobile solution for Pterygium detection, an eye disease, using a classification model based on the convolutional neural network (CNN) architecture ResNext50 in images of the anterior segment of the eye. Four models (ResNext50, ResNet50, MobileNet v2, and DenseNet201) were used for the analysis, with ResNext50 standing out for its high accuracy and diagnostic efficiency. The research, focused on applications for ophthalmological medical centers in Lima, Peru, explains the process of development and integration of the ResNext50 model into a mobile application. The results indicate the high effectiveness of the system, highlighting its high precision, recall, and specificity, which exceed 85%, thus showing its potential as an advanced diagnostic tool in ophthalmology. This system represents a significant tool in ophthalmology, especially for areas with limited access to specialists, offering a rapid and reliable diagnosis of Pterygium. The study also addresses the technical challenges and clinical implications of implementing this technology in a real-world context.","PeriodicalId":507997,"journal":{"name":"International Journal of Online and Biomedical Engineering (iJOE)","volume":"137 30","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141114984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-21DOI: 10.3991/ijoe.v20i08.46499
H. Massar, T. B. Drissi, B. Nsiri, Mounia Miyara
The electroencephalogram (EEG) is a crucial tool for studying brain activity; yet it frequently encounters artifacts that distort meaningful neural signals. This paper addresses the challenge of artifact removal through a unique hybrid method, combining Variational Mode Decomposition (VMD) techniques with Blind Source Separation (BSS) algorithms. VMD, recognized for its adaptability to non-linear and non-stationary EEG data, as well as its ability to alleviate mode mixing and the “endpoint effect,” which serves as an effective preprocessing step. The paper evaluates the performance of two integrated BSS algorithms, AMICA and AMUSE, across various criteria. Comparisons across metrics such as Euclidean distance, Spearman correlation coefficient, and Root Mean Square Error reveal similar performance between AMICA and AMUSE. However, a distinct divergence is evident in the Signal to Artifact Ratio (SAR). When employed with VMD, AMICA demonstrates superiority in effectively discerning and segregating brain signals from artifacts, which gives a mean value of 1.0924. This study introduces a potent hybrid VMDBSS approach for enhancing EEG signal quality. The findings emphasize the notable impact of AMICA, particularly in achieving optimal results in artifact removal, as indicated by its superior performance in SAR. The abstract concludes by underlining the significance of these results, emphasizing AMICA’s pivotal role in achieving the highest measurable evaluation value, making it a compelling choice for researchers and practitioners in EEG signal processing.
{"title":"Improvements of EEG Signal Quality: A Hybrid Method of Blind Source Separation and Variational Mode Destruction to Reduce Artifacts","authors":"H. Massar, T. B. Drissi, B. Nsiri, Mounia Miyara","doi":"10.3991/ijoe.v20i08.46499","DOIUrl":"https://doi.org/10.3991/ijoe.v20i08.46499","url":null,"abstract":"The electroencephalogram (EEG) is a crucial tool for studying brain activity; yet it frequently encounters artifacts that distort meaningful neural signals. This paper addresses the challenge of artifact removal through a unique hybrid method, combining Variational Mode Decomposition (VMD) techniques with Blind Source Separation (BSS) algorithms. VMD, recognized for its adaptability to non-linear and non-stationary EEG data, as well as its ability to alleviate mode mixing and the “endpoint effect,” which serves as an effective preprocessing step. The paper evaluates the performance of two integrated BSS algorithms, AMICA and AMUSE, across various criteria. Comparisons across metrics such as Euclidean distance, Spearman correlation coefficient, and Root Mean Square Error reveal similar performance between AMICA and AMUSE. However, a distinct divergence is evident in the Signal to Artifact Ratio (SAR). When employed with VMD, AMICA demonstrates superiority in effectively discerning and segregating brain signals from artifacts, which gives a mean value of 1.0924. This study introduces a potent hybrid VMDBSS approach for enhancing EEG signal quality. The findings emphasize the notable impact of AMICA, particularly in achieving optimal results in artifact removal, as indicated by its superior performance in SAR. The abstract concludes by underlining the significance of these results, emphasizing AMICA’s pivotal role in achieving the highest measurable evaluation value, making it a compelling choice for researchers and practitioners in EEG signal processing.","PeriodicalId":507997,"journal":{"name":"International Journal of Online and Biomedical Engineering (iJOE)","volume":"28 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141113604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-21DOI: 10.3991/ijoe.v20i08.48387
L. Muflikhah, Amira G. Nurfansepta, Fitra A. Bachtiar, Dian E. Ratnawati
Diabetes may lead to several problems, one of the most prevalent and deadly of which is diabetic nephropathy. Therefore, the condition represents a significant threat to one’s health since it has the potential to cause irreversible harm to the kidneys’ ability to operate. A significant portion of the research that is being conducted now is focused on determining how accurately diabetic people may be predicted to develop kidney illness. Considering this, the research suggests a regression stacking approach for predicting albumin levels. These albumin values will serve as a reference for the incidence of diabetic nephropathy disease. They will be derived from the medical records of patients. The utilization of stacking regression from three different ensemble approaches, using Random Forest and CatBoost regressors, while the Huber algorithm is used as a meta-learner. The accuracy with which the combination of parameters that are employed is determined is a significant factor. It contributes to the high degree of performance that the ensemble approach achieves. Therefore, in this investigation, a grid search was carried out to tune the hyperparameters of both regressor models. We evaluated the performance of the proposed model using accuracy, MAPE, RMSE, and MSE values. The experimental findings demonstrate great performance. Three selected variables including quantitative UACR, semi-quantitative UACR, and urinary creatinine, achieved high performance. Overall, the performance obtained an accuracy rate of more than 98% with an error rate (MAPE, RMSE, and MSE values) of less than 1%. In conclusion, the stack regressor model can be implemented to predict diabetic nephropathy using clinical datasets.
{"title":"High Performance for Predicting Diabetic Nephropathy Using Stacking Regression of Ensemble Learning Method","authors":"L. Muflikhah, Amira G. Nurfansepta, Fitra A. Bachtiar, Dian E. Ratnawati","doi":"10.3991/ijoe.v20i08.48387","DOIUrl":"https://doi.org/10.3991/ijoe.v20i08.48387","url":null,"abstract":"Diabetes may lead to several problems, one of the most prevalent and deadly of which is diabetic nephropathy. Therefore, the condition represents a significant threat to one’s health since it has the potential to cause irreversible harm to the kidneys’ ability to operate. A significant portion of the research that is being conducted now is focused on determining how accurately diabetic people may be predicted to develop kidney illness. Considering this, the research suggests a regression stacking approach for predicting albumin levels. These albumin values will serve as a reference for the incidence of diabetic nephropathy disease. They will be derived from the medical records of patients. The utilization of stacking regression from three different ensemble approaches, using Random Forest and CatBoost regressors, while the Huber algorithm is used as a meta-learner. The accuracy with which the combination of parameters that are employed is determined is a significant factor. It contributes to the high degree of performance that the ensemble approach achieves. Therefore, in this investigation, a grid search was carried out to tune the hyperparameters of both regressor models. We evaluated the performance of the proposed model using accuracy, MAPE, RMSE, and MSE values. The experimental findings demonstrate great performance. Three selected variables including quantitative UACR, semi-quantitative UACR, and urinary creatinine, achieved high performance. Overall, the performance obtained an accuracy rate of more than 98% with an error rate (MAPE, RMSE, and MSE values) of less than 1%. In conclusion, the stack regressor model can be implemented to predict diabetic nephropathy using clinical datasets.","PeriodicalId":507997,"journal":{"name":"International Journal of Online and Biomedical Engineering (iJOE)","volume":"34 41","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141117761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-21DOI: 10.3991/ijoe.v20i08.48071
Suryani, Faizal
Real-time heart rate monitoring and early detection of heart abnormalities are vital to determine heart health before it worsens. To achieve this goal, this project uses the backpropagation neural network (BPNN) method including its capability to classify heartbeats into normal or abnormal by inputting heartbeat values in BPM units derived from prototypes utilizing sensors like Sensor Easy Pulse and NodeMCU, along with considerations of age and sports activity. All data from sensors will be stored in Firebase. Then Firebase will connect to Android, and the normal and abnormal heart classification results will be displayed on the Android system. Simulation results successfully examined 40 people as a sample and provided information from real-time heart rate monitoring, age, and sports activity as input. This research seeks to contribute to improving health services at various public health service centers and independently in detecting heart health early.
{"title":"Prototype Realtime Detection Of Abnormal Heart Beat Using Multiple Back Propagation Neural Network (BPNN)","authors":"Suryani, Faizal","doi":"10.3991/ijoe.v20i08.48071","DOIUrl":"https://doi.org/10.3991/ijoe.v20i08.48071","url":null,"abstract":"Real-time heart rate monitoring and early detection of heart abnormalities are vital to determine heart health before it worsens. To achieve this goal, this project uses the backpropagation neural network (BPNN) method including its capability to classify heartbeats into normal or abnormal by inputting heartbeat values in BPM units derived from prototypes utilizing sensors like Sensor Easy Pulse and NodeMCU, along with considerations of age and sports activity. All data from sensors will be stored in Firebase. Then Firebase will connect to Android, and the normal and abnormal heart classification results will be displayed on the Android system. Simulation results successfully examined 40 people as a sample and provided information from real-time heart rate monitoring, age, and sports activity as input. This research seeks to contribute to improving health services at various public health service centers and independently in detecting heart health early.","PeriodicalId":507997,"journal":{"name":"International Journal of Online and Biomedical Engineering (iJOE)","volume":"7 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141118477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-21DOI: 10.3991/ijoe.v20i08.47883
Didik Hariyanto, Vando Gusti Al Hakim, Amelia Fauziah Husna, R. Badarudin, Nurhening Yuniarti, D. Adinda
This study presents a comprehensive evaluation of a virtual reality-based testing station designed for flexible manufacturing systems. Given the intricate nature of flexible manufacturing systems and the demand for precision in learning, the integration of virtual reality emerges as a promising approach to enhance both student competence and engagement. By employing a combined assessment with the System Usability Scale and heuristic evaluation conducted by 36 students and 5 experts, respectively, the virtual reality-based testing station achieved an average usability score of 72.78, indicating good usability. Noteworthy heuristic challenges, particularly in the domains of ‘Realistic Feedback’ and ‘Navigation and Orientation Support,’ have been identified, providing valuable insights for potential refinements to the testing station. The outcomes of this study not only guide immediate improvements but also pave the way for future research endeavors aimed at elevating the learning outcomes in flexible manufacturing systems courses.
{"title":"Investigating the Efficacy of a Virtual Reality-Based Testing Station of Flexible Manufacturing System: A Usability and Heuristic Evaluation","authors":"Didik Hariyanto, Vando Gusti Al Hakim, Amelia Fauziah Husna, R. Badarudin, Nurhening Yuniarti, D. Adinda","doi":"10.3991/ijoe.v20i08.47883","DOIUrl":"https://doi.org/10.3991/ijoe.v20i08.47883","url":null,"abstract":"This study presents a comprehensive evaluation of a virtual reality-based testing station designed for flexible manufacturing systems. Given the intricate nature of flexible manufacturing systems and the demand for precision in learning, the integration of virtual reality emerges as a promising approach to enhance both student competence and engagement. By employing a combined assessment with the System Usability Scale and heuristic evaluation conducted by 36 students and 5 experts, respectively, the virtual reality-based testing station achieved an average usability score of 72.78, indicating good usability. Noteworthy heuristic challenges, particularly in the domains of ‘Realistic Feedback’ and ‘Navigation and Orientation Support,’ have been identified, providing valuable insights for potential refinements to the testing station. The outcomes of this study not only guide immediate improvements but also pave the way for future research endeavors aimed at elevating the learning outcomes in flexible manufacturing systems courses.","PeriodicalId":507997,"journal":{"name":"International Journal of Online and Biomedical Engineering (iJOE)","volume":"46 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141116856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-21DOI: 10.3991/ijoe.v20i08.47439
L. Moharana, Niva Das, A. Routray
Stress is a state of mental tension, which helps us to cope with challenges in our life. It makes us progressive when it is positive, but excessive negative stress that perseveres for a long time leads to a state of depressiveness. Longer stressed stage of a human being changes the size, functionality and frequency of response of many internal and external body parameters. By applying computer vision techniques, these changes of body parameters can be tracked to get useful information about the mental stress for a stress affected person. Many studies show the pupil diameter varies significantly with the effect of stress. Our work is based on the study of variation of pupil diameters of stress affected and not affected university students. With the application of different supervised machine learning algorithms, we have observed that the pupil dilates more in case of stress affected students than non-stressed students. We have also found that the pupils of the students dilates more when they were in positive emotional states than their negative emotional states. This work will be helpful for researchers who are working in the field of emotion detection and recognition and affective disorder analysis.
{"title":"Application of Computer Vision Techniques to Study the Relationship between Mental Stress and Pupil Diameter among Student Population","authors":"L. Moharana, Niva Das, A. Routray","doi":"10.3991/ijoe.v20i08.47439","DOIUrl":"https://doi.org/10.3991/ijoe.v20i08.47439","url":null,"abstract":"Stress is a state of mental tension, which helps us to cope with challenges in our life. It makes us progressive when it is positive, but excessive negative stress that perseveres for a long time leads to a state of depressiveness. Longer stressed stage of a human being changes the size, functionality and frequency of response of many internal and external body parameters. By applying computer vision techniques, these changes of body parameters can be tracked to get useful information about the mental stress for a stress affected person. Many studies show the pupil diameter varies significantly with the effect of stress. Our work is based on the study of variation of pupil diameters of stress affected and not affected university students. With the application of different supervised machine learning algorithms, we have observed that the pupil dilates more in case of stress affected students than non-stressed students. We have also found that the pupils of the students dilates more when they were in positive emotional states than their negative emotional states. This work will be helpful for researchers who are working in the field of emotion detection and recognition and affective disorder analysis.","PeriodicalId":507997,"journal":{"name":"International Journal of Online and Biomedical Engineering (iJOE)","volume":"18 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141113915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-21DOI: 10.3991/ijoe.v20i08.48345
Aulia Asman, Yulkifli, Yohandri, Naurah Nazhifah, Soha Rawas, A. Samala
Intravenous (IV) infusion is a pervasive medical intervention, administered to approximately 90% of hospitalized patients. Phlebitis, characterized by inflammation of the veins resulting from infusion, stands as a prevalent complication, ranking fourth among hospitalacquired infections globally. This research investigates the efficacy of a Smartphone Early Warning System (EWS) display in mitigating the incidence of phlebitis within the Safa treatment room at Aisyiyah Hospital. Employing a pre-experimental research design with a Static-group Comparison approach, 16 respondents were allocated to treatment and control groups. The Mann-Whitney Test, a statistical analysis, unveiled a significant difference (P Value = 0.001 < 0.05) in phlebitis incidence between the treatment group, utilizing the Smartphone EWS display, and the control group, which relied on conventional monitoring methods. Notably, the average rank of phlebitis incidence in the control group (21.12) exceeded that in the treatment group (9.78). This study sheds light on the potential of the Smartphone EWS display to curtail phlebitis during infusion, emphasizing its role in advancing nursing care quality through real-time monitoring and early prevention strategies.
{"title":"Safeguarding Vascular Health: Unleashing the Potential of Smartphone Early Warning Systems to Elevate Phlebitis Prevention in IV Infusion Therapy","authors":"Aulia Asman, Yulkifli, Yohandri, Naurah Nazhifah, Soha Rawas, A. Samala","doi":"10.3991/ijoe.v20i08.48345","DOIUrl":"https://doi.org/10.3991/ijoe.v20i08.48345","url":null,"abstract":"Intravenous (IV) infusion is a pervasive medical intervention, administered to approximately 90% of hospitalized patients. Phlebitis, characterized by inflammation of the veins resulting from infusion, stands as a prevalent complication, ranking fourth among hospitalacquired infections globally. This research investigates the efficacy of a Smartphone Early Warning System (EWS) display in mitigating the incidence of phlebitis within the Safa treatment room at Aisyiyah Hospital. Employing a pre-experimental research design with a Static-group Comparison approach, 16 respondents were allocated to treatment and control groups. The Mann-Whitney Test, a statistical analysis, unveiled a significant difference (P Value = 0.001 < 0.05) in phlebitis incidence between the treatment group, utilizing the Smartphone EWS display, and the control group, which relied on conventional monitoring methods. Notably, the average rank of phlebitis incidence in the control group (21.12) exceeded that in the treatment group (9.78). This study sheds light on the potential of the Smartphone EWS display to curtail phlebitis during infusion, emphasizing its role in advancing nursing care quality through real-time monitoring and early prevention strategies.","PeriodicalId":507997,"journal":{"name":"International Journal of Online and Biomedical Engineering (iJOE)","volume":"69 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141116774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-21DOI: 10.3991/ijoe.v20i08.49343
Ramesh Kumar, Mohammad Aljaidi, Manish Kumar Singla, Anupma Gupta, A. Alhomoud, Amjad A. Alsuwaylimi, Sami M. Alenezi
This paper presents a novel vision impairment assistive device to improve mobility and independence. This device consists of Arduino Nano microcontroller technology that powers the Satellite/GPS-based stick, which tracks and navigates in real-time. Arduino Nano’s adaptability and compactness enable our portable, affordable white cane replacement. Satellite signals let the stick locate the user, compute the best routes, and provide aural navigation cues through speakers or headphones. The obstacle detection sensors notify users of adjacent risks, improving safety. The proposed device is a stable and user-friendly technology that delivers a potential answer to visually impaired navigation issues after rigorous development and testing.
{"title":"Development of a Prototype Global Positioning System Based Stick for Blind Patients","authors":"Ramesh Kumar, Mohammad Aljaidi, Manish Kumar Singla, Anupma Gupta, A. Alhomoud, Amjad A. Alsuwaylimi, Sami M. Alenezi","doi":"10.3991/ijoe.v20i08.49343","DOIUrl":"https://doi.org/10.3991/ijoe.v20i08.49343","url":null,"abstract":"This paper presents a novel vision impairment assistive device to improve mobility and independence. This device consists of Arduino Nano microcontroller technology that powers the Satellite/GPS-based stick, which tracks and navigates in real-time. Arduino Nano’s adaptability and compactness enable our portable, affordable white cane replacement. Satellite signals let the stick locate the user, compute the best routes, and provide aural navigation cues through speakers or headphones. The obstacle detection sensors notify users of adjacent risks, improving safety. The proposed device is a stable and user-friendly technology that delivers a potential answer to visually impaired navigation issues after rigorous development and testing.","PeriodicalId":507997,"journal":{"name":"International Journal of Online and Biomedical Engineering (iJOE)","volume":"32 32","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141117877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-06DOI: 10.3991/ijoe.v20i07.47483
O. Stitini, Fathia Ouakasse, Said Rakrak, S. Kaloun, Omar Bencharef
The widespread adoption of the Internet of Things has significantly enhanced our daily lives across various dimensions. E-health has significantly benefited from advancements in the Internet of Things (IoT), particularly with the emergence of the Internet of Medical Things (IoMT). A sophisticated wireless sensor network produces a huge amount of data, requiring robust cloud-based hardware for precise processing and categorization. The IoMT allows for the extensive gathering of medical data from incoming hospital patients, enabling real-time monitoring of vital signs and health statuses. Nevertheless, effectively prioritizing patients in emergencies is challenging due to the importance and complicatedness of the data. To tackle this issue, an innovative solution involves integrating Explainable Artificial Intelligence into the IoMT ecosystem. By incorporating Explainable AI, the system enhances explainability, fostering trust and reliability in patient prioritization. This provides healthcare providers a more reliable prioritization mechanism that aligns with established medical guidelines. The study explores IoMT devices for collecting medical data from incoming patients, focusing on the MQTT protocol for lightweight devices, aiming to guide patients to the right department and prioritize emergency management through IoMT data analysis.
{"title":"Combining IoMT and XAI for Enhanced Triage Optimization: An MQTT Broker Approach with Contextual Recommendations for Improved Patient Priority Management in Healthcare","authors":"O. Stitini, Fathia Ouakasse, Said Rakrak, S. Kaloun, Omar Bencharef","doi":"10.3991/ijoe.v20i07.47483","DOIUrl":"https://doi.org/10.3991/ijoe.v20i07.47483","url":null,"abstract":"\u0000\u0000\u0000\u0000The widespread adoption of the Internet of Things has significantly enhanced our daily lives across various dimensions. E-health has significantly benefited from advancements in the Internet of Things (IoT), particularly with the emergence of the Internet of Medical Things (IoMT). A sophisticated wireless sensor network produces a huge amount of data, requiring robust cloud-based hardware for precise processing and categorization. The IoMT allows for the extensive gathering of medical data from incoming hospital patients, enabling real-time monitoring of vital signs and health statuses. Nevertheless, effectively prioritizing patients in emergencies is challenging due to the importance and complicatedness of the data. To tackle this issue, an innovative solution involves integrating Explainable Artificial Intelligence into the IoMT ecosystem. By incorporating Explainable AI, the system enhances explainability, fostering trust and reliability in patient prioritization. This provides healthcare providers a more reliable prioritization mechanism that aligns with established medical guidelines. The study explores IoMT devices for collecting medical data from incoming patients, focusing on the MQTT protocol for lightweight devices, aiming to guide patients to the right department and prioritize emergency management through IoMT data analysis. \u0000\u0000\u0000\u0000","PeriodicalId":507997,"journal":{"name":"International Journal of Online and Biomedical Engineering (iJOE)","volume":"67 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141007807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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