Early Detection and Diagnosis of Chronic Kidney Disease Based on Selected Predominant Features
Abstract and Figures
In numerous perilous cases, a quick medical decision is needed for the early detection of chronic diseases to avoid austere consequences that may be fatal. Chronic kidney disease (CKD) is a prevalent disease that presents a variety of challenges, including soaring costs for intervention, urgency, and, more importantly, difficulty in early detection of the disease. The current study carries out a prediction-based method that helps in detecting and diagnosing CKD patients which enables a fast and accurate decision-making process at the early stage. A combination of preprocessing and feature selection methods was developed; additionally, several prediction models, such as K-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and bagging, were trained based on the processed dataset. The performance evaluation shows higher reliability of all models in terms of accuracy, precision, sensitivity, F-measure, specificity, and area under the curve (AUC) score. Specifically, KNN outperformed with an accuracy of 99.50%, sensitivity of 99.2%, precision of 100%, specificity of 98.7%, and F-measure and AUC score of 99.6%. The experimental results of KNN show the best fitted model compared to the existing state-of-the-art methods. Moreover, the reduced feature set proves that just a few clinical tests are enough to detect CKD, resulting in diagnosis cost reduction.
![Dataset description and values [22].](https://www.researchgate.net/publication/367552042/figure/fig1/AS:11431281116274008@1675210818633/Dataset-description-and-values-22_Q320.jpg)
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With the rise of the Internet and social media, information has become available at our fingertips. However, on the dark side, these advancements have opened doors for fraudsters. Online recruitment fraud (ORF) is one of the problems created by these modern technologies, as hundreds of thousands of applicants are victimized every year globally. Fraudsters advertise bogus jobs on online platforms and target job hunters with fake offerings such as huge salaries and desirable geographical locations. The objective of these fraudsters is to collect personal information to be misused in the future, leading to the loss of applicants’ privacy. To prevent such situations, there is a need for an automatic detecting system that can distinguish between real and fake job advertisements and preserve the applicants’ privacy. This study attempts to build a smart secured framework for detecting and preventing ORF using ensemble machine learning (ML) techniques. In this regard, four ensemble methods—AdaBoost (AB), Xtreme Gradient Boost (XGB), Voting, and Random Forest (RF)—are used to build a detection framework. The dataset used was pre-processed using several methods for cleaning and denoising in order to achieve better outcomes. The performance evaluation measures of the applied methods were accuracy, precision, sensitivity, F-measure, and ROC curves. According to these measures, AB performed best, followed by XGB, voting, and RF. In the proposed framework, AB achieved a high accuracy of 98.374%, showing its reliability for detecting and preventing ORF. The results of AB were compared to existing methods in the literature validating the reliability of the model to be significantly used for detecting ORF.
Currently, digital transformation has occurred in most countries in the world to varying degrees, but digitizing business processes are complex in terms of understanding the various aspects of manual documentation. The use of digital devices and intelligent systems is vital in the digital transformation of manual documentation from hardcopy to digital formats. The transformation of handwritten documents into electronic files is one of the principal aspects of digitization and represents a common need shared by today’s businesses. Generally, handwriting recognition poses a complex digitization challenge, and Arabic handwriting recognition, specifically, proves inordinately challenging due to the nature of Arabic scripts and the excessive diversity in human handwriting. This study presents an intelligent approach for recognizing handwritten Arabic letters. In this approach, a convolution neural network (CNN) model is proposed to recognize handwritten Arabic letters. The model is regularized using batch normalization and dropout operations. Moreover, the model was tested with and without dropout, resulting in a significant difference in the performance. Hence, the model overfitting has been prevented using dropout regularization. The proposed model was applied to the prominent, publicly-available Arabic handwritten characters (AHCD) dataset with 16,800 letters, and the performance was measured using several evaluation measures. The experimental results show the best fit of the proposed model in terms of higher accuracy results that reached 96.78%; additionally, other evaluation measures compared to popular domain-relevant approaches in the literature.
Alzheimer's disease (AD) is a chronic and common form of dementia that mainly affects elderly individuals. The disease is dangerous because it causes damage to brain cells and tissues before the symptoms appear, and there is no medicinal or surgical treatment available yet for AD. AD causes loss of memory and functionality control in multiple degrees according to AD's progression level. However, early diagnosis of AD can hinder its progression. Brain imaging tools such as magnetic resonance imaging (MRI), computed tomography (CT) scans, positron emission tomography (PET), etc. can help in medical diagnosis of AD. Recently, computer-aided diagnosis (CAD) such as deep learning applied to brain images obtained with these tools, has been an established strategic methodology that is widely used for clinical assistance in prognosis of AD. In this study, we proposed an intelligent methodology for building a convolutional neural network (CNN) from scratch to detect AD stages from the brain MRI images dataset and to improve patient care. It is worth mentioning that training a deep-learning model requires a large amount of data to produce accurate results and prevent the model from overfitting problems. Therefore, for better understanding of classifiers and to overcome the model overfitting problem, we applied data augmentation to the minority classes in order to increase the number of MRI images in the dataset. All experiments were conducted using Alzheimer's MRI dataset consisting of brain MRI scanned images. The performance of the proposed model determines detection of the four stages of AD. Experimental results show high performance of the proposed model in that the model achieved a 99.38% accuracy rate, which is the highest so far. Moreover, the proposed model performance in terms of accuracy, precision, sensitivity, specificity, and f-measures is promising when compared to the very recent state-of-the-art domain-specific models existing in the literature.
Currently, digital transformation has occurred in most countries in the world to varying degrees, but digitizing business processes are complex in terms of understanding the various aspects of manual documentation. The use of digital devices and intelligent systems is vital in the digital transformation of manual documentation from hardcopy to digital formats. The transformation of handwritten documents into electronic files is one of the principal aspects of digitization and represents a common need shared by today’s businesses. Generally, handwriting recognition poses a complex digitization challenge, and Arabic handwriting recognition, specifically, proves inordinately challenging due to the nature of Arabic scripts and the excessive diversity in human handwriting. This study presents an intelligent approach for recognizing handwritten Arabic letters. In this approach, a convolution neural network (CNN) model is proposed to recognize handwritten Arabic letters. The model is regularized using batch normalization and dropout operations. Moreover, the model was tested with and without dropout, resulting in a significant difference in the performance. Hence, the model overfitting has been prevented using dropout regularization. The proposed model was applied to the prominent, publicly-available Arabic handwritten characters (AHCD) dataset with 16,800 letters, and the performance was measured using several evaluation measures. The experimental results show the best fit of the proposed model in terms of higher accuracy results that reached 96.78%; additionally, other evaluation measures compared to popular domain-relevant approaches in the literature.
A SARS-CoV-2 virus-specific reverse transcriptase-polymerase chain reaction (RT-PCR) test is usually used to diagnose COVID-19. However, this test requires up to 2 days for completion. Moreover, to avoid false-negative outcomes, serial testing may be essential. The availability of RT-PCR test kits is currently limited, highlighting the need for alternative approaches for the precise and rapid diagnosis of COVID-19. Patients suspected to be infected with SARS-CoV-2 can be assessed using chest CT scan images. However, CT images alone cannot be used for ruling out SARS-CoV-2 infection because individual patients may exhibit normal radiological results in the primary phases of the disease. A machine learning (ML)-based recognition and segmentation system was developed to spontaneously discover and compute infection areas in CT scans of COVID-19 patients. The computable assessment exhibited suitable performance for automatic infection region allocation. The ML models developed were suitable for the direct detection of COVID-19 (+). ML was confirmed to be a complementary diagnostic technique for diagnosing COVID-19(+) by forefront medical specialists. The complete manual delineation of COVID-19 often requires up to 225.5 min; however, the proposed RILML method decreases the delineation time to 7 min after four iterations of model updating.
The amount of produced data in industrial environments is continuously rising, containing relevant but also irrelevant or redundant data. The size and dimensionality of gathered data makes it difficult to identify features that have a high influence on a given target using domain information alone. Therefore, some kind of dimensionality reduction is necessary to extract the most important features. To provide an explainable feature selection process and avoid the creation of less explainable features, filter feature selection methods provide good approaches. Existing surveys cover a broad range of methods, however the provided comparative analysis is often restricted to a limited set of methods or to a limited number of datasets. This paper will cover a broad range of filter feature selection methods and compare them in three scenarios, ranging from low to high dimensional data, to determine their usability in varying settings.
Patients with deaths from COVID-19 often have co-morbid cardiovascular disease. Real-time cardiovascular disease monitoring based on wearable medical devices may effectively reduce COVID-19 mortality rates. However, due to technical limitations, there are three main issues. First, the traditional wireless communication technology for wearable medical devices is difficult to satisfy the real-time requirements fully. Second, current monitoring platforms lack efficient streaming data processing mechanisms to cope with the large amount of cardiovascular data generated in real time. Third, the diagnosis of the monitoring platform is usually manual, which is challenging to ensure that enough doctors online to provide a timely, efficient, and accurate diagnosis. To address these issues, this paper proposes a 5G-enabled real-time cardiovascular monitoring system for COVID-19 patients using deep learning. Firstly, we employ 5G to send and receive data from wearable medical devices. Secondly, Flink streaming data processing framework is applied to access electrocardiogram data. Finally, we use convolutional neural networks and long short-term memory networks model to obtain automatically predict the COVID-19 patient’s cardiovascular health. Theoretical analysis and experimental results show that our proposal can well solve the above issues and improve the prediction accuracy of cardiovascular disease to 99.29%.
Background
The use of artificial intelligence has revolutionized every area of life such as business and trade, social and electronic media, education and learning, manufacturing industries, medicine and sciences, and every other sector. The new reforms and advanced technologies of artificial intelligence have enabled data analysts to transmute raw data generated by these sectors into meaningful insights for an effective decision-making process. Health care is one of the integral sectors where a large amount of data is generated daily, and making effective decisions based on these data is therefore a challenge. In this study, cases related to childbirth either by the traditional method of vaginal delivery or cesarean delivery were investigated. Cesarean delivery is performed to save both the mother and the fetus when complications related to vaginal birth arise.
Objective
The aim of this study was to develop reliable prediction models for a maternity care decision support system to predict the mode of delivery before childbirth.
Methods
This study was conducted in 2 parts for identifying the mode of childbirth: first, the existing data set was enriched and second, previous medical records about the mode of delivery were investigated using machine learning algorithms and by extracting meaningful insights from unseen cases. Several prediction models were trained to achieve this objective, such as decision tree, random forest, AdaBoostM1, bagging, and k-nearest neighbor, based on original and enriched data sets.
Results
The prediction models based on enriched data performed well in terms of accuracy, sensitivity, specificity, F-measure, and receiver operating characteristic curves in the outcomes. Specifically, the accuracy of k-nearest neighbor was 84.38%, that of bagging was 83.75%, that of random forest was 83.13%, that of decision tree was 81.25%, and that of AdaBoostM1 was 80.63%. Enrichment of the data set had a good impact on improving the accuracy of the prediction process, which supports maternity care practitioners in making decisions in critical cases.
Conclusions
Our study shows that enriching the data set improves the accuracy of the prediction process, thereby supporting maternity care practitioners in making informed decisions in critical cases. The enriched data set used in this study yields good results, but this data set can become even better if the records are increased with real clinical data.
Objective:
Chronic kidney disease (CKD) is a major public health concern worldwide. High costs of late-stage diagnosis and insufficient testing facilities can contribute to high morbidity and mortality rates in CKD patients, particularly in less developed countries. Thus, early diagnosis aided by vital parameter analytics using affordable computer-aided diagnosis could not only reduce diagnosis costs but improve patient management and outcomes.
Methods:
In this study, we developed machine learning models using selective key pathological categories to identify clinical test attributes that will aid in accurate early diagnosis of CKD. Such an approach will save time and costs for diagnostic screening. We have also evaluated the performance of several classifiers with k-fold cross-validation on optimized datasets derived using these selected clinical test attributes.
Results:
Our results suggest that the optimized datasets with important attributes perform well in diagnosis of CKD using our proposed machine learning models. Furthermore, we evaluated clinical test attributes based on urine and blood tests along with clinical parameters that have low costs of acquisition. The predictive models with the optimized and pathologically categorized attributes set yielded high levels of CKD diagnosis accuracy with random forest (RF) classifier being the best performing.
Conclusions:
Our machine learning approach has yielded effective predictive analytics for CKD screening which can be developed as a resource to facilitate improved CKD screening for enhanced and timely treatment plans.
Lying on the dynamic capabilities perspective, organizational citizenship behavior for the environment and literature on green environmental management and performance as theoretical underpinnings, this study examines how adaptive capability and environmental behavior affect corporate sustainability performance and financial performance. To this aim, it relies on the use of the partial least squares-structural equation modeling (PLS-SEM) approach to analyze the data collected from 311 ISO 14001 certified firms in Saudi Arabia. The empirical analysis of the data reveals that, as it was hypothesized, both adaptive capability and environmental behavior have a positive and significant effect on corporate sustainability performance as well as on financial performance. In addition, the results demonstrate that corporate sustainability performance partially mediates the links between the two antecedent (exogenous) constructs and the endogenous construct (financial performance). This study may provide useful implications for cleaner production scholars and practitioners, that may enable them to better grasp the importance of training their employees in competencies such as adaptation and change detection and management, as well as to develop a pro-environmental behavior.
A SARS-CoV-2 virus-specific reverse transcriptase-polymerase chain reaction (RT-PCR) test is usually used to diagnose COVID-19. However, this test requires up to 2 days for completion. Moreover, to avoid false-negative outcomes, serial testing may be essential. The availability of RT-PCR test kits is currently limited, highlighting the need for alternative approaches for the precise and rapid diagnosis of COVID-19. Patients suspected to be infected with SARS-CoV-2 can be assessed using chest CT scan images. However, CT images alone cannot be used for ruling out SARS-CoV-2 infection because individual patients may exhibit normal radiological results in the primary phases of the disease. A machine learning (ML)-based recognition and segmentation system was developed to spontaneously discover and compute infection areas in CT scans of COVID-19 patients. The computable assessment exhibited suitable performance for automatic infection region allocation. The ML models developed were suitable for the direct detection of COVID-19 (?). ML was confirmed to be a complementary diagnostic technique for diagnosing COVID-19(?) by forefront medical specialists. The complete manual delineation of COVID-19 often requires up to 225.5 min; however, the proposed RILML method decreases the delineation time to 7 min after four iterations of model updating.