Preemptive Diagnosis of Chronic Kidney Disease Using Machine Learning Techniques

Alassaf, Reem; Alsulaim, Khawla A.; Alroomi, Noura Y.; Alsharif, Nouf S.; Aljubeir, Mishael F.; Olatunji, Sunday O.; Alahmadi, Alaa; Imran, Mohammed; Alzahrani, Rahma A.; Alturayeif, Nora

doi:10.1109/innovations.2018.8606040

Cited by 30 publications

(9 citation statements)

References 10 publications

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“…Several other machine learning, deep learning and AI based techniques have been applied for disease predictions in the literature and it is among the hottest areas of research in healthcare and several other disciplines [17][18][19][20][21][22][23][24][25][26][27][28][29][30].…”

Section: Related Workmentioning

confidence: 99%

Ensemble Machine Learning Based Identification of Adult Epilepsy

Ahmed¹,

Zaghdoud²,

Al-Abdulqader³

et al. 2023

MMEP

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Epilepsy is a chronic non-communicable illness that affects brain individuals and impacts more than 50 million people globally. To predict epileptic seizures, we proposed machine learning-based ensemble learning technique in this study. In the preprocessed stage, we applied some important techniques such as Power line noise reduction and dividing the record into windows of 5 seconds. The project is created by the help of ensemble machine learning technique, which employs several machine learning algorithms, we used the following algorithms: decision tree, support vector machine, artificial neural networks, and convolutional neural networks. We used a dataset from PhysioNet website that contains adult EEG signals. Several convolutional layers were used to extract features from the EEG signals, after that, the feature set is utilized to train a classifier model, which combines the results. Our approach successfully reached 91% accuracy while 91% sensitivity and 91% specificity, respectively.

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Section: Related Workmentioning

confidence: 99%

Ensemble Machine Learning Based Identification of Adult Epilepsy

Ahmed¹,

Zaghdoud²,

Al-Abdulqader³

et al. 2023

MMEP

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“…Furthermore, SVM has proved itself as a powerful technique that shows an excellent performance in various fields such as spam email detection [20], chronic kidney disease diagnosing [21], and failure prediction in cloud computing [22,11].…”

Section: Support Vector Machinementioning

confidence: 99%

Task Failure Prediction Using Machine Learning Techniques in the Google Cluster Trace Cloud Computing Environment

Gollapalli¹,

AlMetrik²,

AlNajrani³

et al. 2022

MMEP

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Cloud computing has grown into a critical technology by enabling ground-breaking capabilities for Internet-dependent computer platforms and software applications. As cloud computing systems continue to expand and develop, the need for a more guaranteed, reliant service, and an early task execution status from Cloud Service Providers (CSP) is vital. Additionally, efficient prediction of task failure significantly improves the running time as well as resource utilization in cloud computing. Task failure forecasting in the cloud is regarded as a challenging task based on the literature review conducted in this study. To address these issues, the goal of this study aimed to create fast machine learning approaches for reliably predicting task failure in cloud computing and analyzing their performance using multiple assessment criteria. The Google cluster dataset was used in this study, coupled with Artificial Neural Network (ANN), Support Vector Machine (SVM), and a stacking ensemble method, to forecast job failure in a cloud computing context. The results show that the proposed models can predict the failed tasks both effectively and efficiently. The stacking ensemble outperformed the experimented models, reaching a 99.8%. The suggested paradigm could greatly benefit cloud service providers by decreasing wasted resources and costs associated with task failures.

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“…Predicting the presence of topographic features within extensive image datasets poses a formidable computational challenge, primarily attributed to diverse factors, including the suboptimal choice of predictive variables, the inherent limitations in dataset size, and the conventional reliance on feature sets in conjunction with machine learning classifiers ( Nandhini & Aravinth, 2021 ; Islam et al, 2020 ; Alassaf et al, 2018 ). Furthermore, the utilization of deep learning models for image prediction has been hampered by the inadequacies in predictor variable selection and the lack of hybridized models.…”

Section: Introductionmentioning

confidence: 99%

DenseHillNet: a lightweight CNN for accurate classification of natural images

Saqib,

Zubair Asghar,

Iqbal

et al. 2024

PeerJ Computer Science

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The detection of natural images, such as glaciers and mountains, holds practical applications in transportation automation and outdoor activities. Convolutional neural networks (CNNs) have been widely employed for image recognition and classification tasks. While previous studies have focused on fruits, land sliding, and medical images, there is a need for further research on the detection of natural images, particularly glaciers and mountains. To address the limitations of traditional CNNs, such as vanishing gradients and the need for many layers, the proposed work introduces a novel model called DenseHillNet. The model utilizes a DenseHillNet architecture, a type of CNN with densely connected layers, to accurately classify images as glaciers or mountains. The model contributes to the development of automation technologies in transportation and outdoor activities. The dataset used in this study comprises 3,096 images of each of the “glacier” and “mountain” categories. Rigorous methodology was employed for dataset preparation and model training, ensuring the validity of the results. A comparison with a previous work revealed that the proposed DenseHillNet model, trained on both glacier and mountain images, achieved higher accuracy (86%) compared to a CNN model that only utilized glacier images (72%). Researchers and graduate students are the audience of our article.

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Preemptive Diagnosis of Chronic Kidney Disease Using Machine Learning Techniques

Cited by 30 publications

References 10 publications

Ensemble Machine Learning Based Identification of Adult Epilepsy

Ensemble Machine Learning Based Identification of Adult Epilepsy

Task Failure Prediction Using Machine Learning Techniques in the Google Cluster Trace Cloud Computing Environment

DenseHillNet: a lightweight CNN for accurate classification of natural images

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