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Analyze the impact of feature selection techniques in the early prediction of CKD

International Journal of Cognitive Computing in engineering 2024年第1期5卷 66-77页

作者： Hema, K. Meena, K. Pandian, Ramaraj Department of Computer Science and Engineering GITAM School of Technology GITAM University Bangalore India Netapp San JoseCA United States

Background: Chronic renal disease, often known as Chronic Kidney Disease (CKD), is an illness that causes a steady decline in kidney function. As per the World Health Organization survey, the incidence of CKD may increase from 10% to 13% by 2030. Because of the lack of symptoms in the initial phase, diagnosing CKD early on may be difficult. The key objective of this study is to develop a forecasting model for the early detection of chronic renal disease. Methods: In medical science, Machine Learning (ML) Techniques play a significant role in disease prediction despite numerous studies conducted to categorize CKD in patients using machine learning tools. Most researchers need to analyze the impact of feature selection techniques, yielding high-quality and reliable results. The efficiency of any Techniques/Algorithms depends on feature selection, feature extraction, and classifiers. In this work, the impact of feature selection is experimented with using the Exhaustive Feature Selection (EFS) method. For the early prediction of CKD, a comparative examination of machine learning classifiers, including Gradient Boost (GB), XGBoost, Decision Tree (DT), Random Forest (RF), and KNN (k-nearest neighbors), are utilized. Results: Two types of datasets, standard (New Model) & real-time data sets collected from the dialysis unit of a reputed hospital in Chennai, are used to carry out extensive experiment analysis. Various metrics, including Accuracy, Precision, Recall, and F1-score, are used to tabulate the results of experiments conducted to measure the performance of the proposed approach for various combinations of test and training data. Conclusion: CKD is an irreversible and silent disease;it might have a high impact on many people and begin to manifest themselves at an early age in life. This research paper analyses the effect of feature selection techniques on early CKD prediction. © 2023 The Authors

关键词： Forecasting

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Enhancing Disease Prediction through Ensemble Learning Techniques 18

Enhancing Disease Prediction through Ensemble Learning Techn...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Wani, Suhail Rashid Attri, Shree Harsh Setia, Sonia Sharda University School of Engineering and Technology Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9789380544519

The study demonstrates how technology has had a dramatic influence on healthcare, allowing for the analysis of large clinical datasets using machine learning for early illness identification. Chronic disorders such as cardiovascular disease (CVD), Chronic renal illness, and Chronic Mellitus Diabetes offer significant global health issues, using existing diagnostic procedures that are time-consuming and error-prone. The report emphasizes the rising burden of chronic illnesses, estimating that 73% of deaths will be related to these ailments by 2025. Recognizing the links between illnesses such as CKD and CVD is critical for optimal care, with a particular emphasis on the increased prevalence and health hazards associated with Chronic Diabetes Mellitus (CDM). The fundamental goal of the research is to create an effective prediction model for early illness diagnosis by employing an adaptable machine learning model that can adapt to a variety of datasets. The review of the literature looks at several machine learning algorithms that have been utilized in prior research, emphasizing their potential for automating illness identification. The methods section describes the procedures required in collecting data, preparing it, and modeling it using ensemble learning. Metrics of performance evaluation confirm the Extra tree classifier's advantage and it performs the best among all the four ensemble techniques and improves the accuracy of predicting diseases. Finally, the above investigation highlights the promise of computational learning in solving the complex issues faced by chronic illnesses, emphasizing the crucial need for accurate early disease prediction in the context of a growing global health crisis. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Cat Boost Chronic Diabetes Mellitus Chronic Disease Computational learning Ensemble Model Feature Selection Heart Disease Random Forest XG Boost

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A Way to Detect Seizure through ML for Enhancing the Epilepsy Way of Management 5

A Way to Detect Seizure through ML for Enhancing the Epileps...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Sharma, Anirudh Khan, Shinora Chhabra, Rithik Kumar, Rajendra Sharda University School of Engineering & Technology Department of Computer Science & Engineering Greater Noida India

ISBN: (纸本)9798350383522

This study unveils a groundbreaking system leveraging the capabilities of machine learning to forecast and identify seizures, thereby making a substantial positive impact on the lives of individuals grappling with seizures and alleviating concerns for their caregivers. Demonstrating an exceptional accuracy rate of 98.9%, machine learning techniques are applied to not only categorize seizures but also to predict their onset. This innovative approach facilitates prompt intervention and personalized support, addressing a critical requirement in healthcare. The system employs a comprehensive methodology, utilizing cutting-edge sensors such as electroencephalography (EEG) for monitoring brain activity, electrocardiography (ECG) for tracking heart rate, and thermography for temperature monitoring. This holistic physiological monitoring provides invaluable insights for healthcare professionals, enabling them to tailor treatment plans based on individual needs. However, the true ingenuity lies in the real-time alert system, which promptly notifies designated caregivers through SMS and email upon detecting a potential seizure. This feature not only ensures swift response times but also offers peace of mind and heightened safety for individuals with seizures and their families. Furthermore, the system is meticulously designed to be user-friendly and seamlessly integrates with assistive technologies, ensuring accessibility for users with varying levels of technical expertise. Essentially, this study offers a ground-breaking approach to seizure detection through machine learning, offering a revolutionary fix. that greatly improves the general well-being and safety of those who have seizures and their families. This is a significant development in medical technology with its comprehensive monitoring and real-time alerts serving as a vital lifeline for individuals in need. © 2024 IEEE.

关键词： EEG-based seizure prediction Epileptic seizure detection Machine learning Seizure detection

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Tri-UnityNet: A Multifaceted Ensemble Model for Pneumonia Detection

Tri-UnityNet: A Multifaceted Ensemble Model for Pneumonia De...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Dhivyaa, C.R. Nithya, K. Shanmugapriya, P. Deebika, R. Kumar, K. Sathis Sudhakar, R. School Of Computer Science And Engineering Vellore Institute Of Technology Vellore India Srm Institute Of Science And Technology Computer Science And Engineering Trichy India Computer Science And Engineering Erode India K.S.R College Of Engineering Computer Science And Engineering Tiruchengode India Bannari Amman Institute Of Technology Computer Science And Engineering Sathyamangalam India Nandha College Of Technology Computer Science And Engineering Erode India

ISBN: (纸本)9798350364828

Pneumonia is an infection often caused by several viral infections and prediction of pneumonia requires expertise from radiotherapists, posing challenges, especially in remote areas. Developing an automatic pneumonia detection system is crucial to detect and treat pneumonia disease. Hence, an innovative approach is proposed to detect pneumonia by utilizing the ensemble of deep learning models, specifically VGG16, ResNet50, and InceptionV3. The ensemble method integrates predictions from these various architectures to enhance overall classification performance. Rather than starting the training of each model from the beginning, transfer learning techniques are utilized to fine-tune Convolutional Neural Network (CNN) models using a pre-existing knowledge base. This involves leveraging pre-trained models and adapting them specifically for the disease diagnose on Chest X-ray image dataset. The features are extracted from VGG16, ResNet50, and InceptionV3 models and the classes of disease are predicted by using softmax layers. Finally, ensemble learning is employed to fuse the predictions of multiple neural network architectures within Tri-UnityNet, enhancing its accuracy and robustness in pneumonia detection. By leveraging the complementary strengths of diverse models, the ensemble approach achieves superior performance, contributing to more reliable diagnostic outcomes. © 2024 IEEE.

关键词： Forecasting

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Intrusion Detection Based on Bidirectional Long Short-Term Memory with Attention Mechanism

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computers, Materials & Continua 2023年第1期74卷 801-815页

作者： Yongjie Yang Shanshan Tu Raja Hashim Ali Hisham Alasmary Muhammad Waqas Muhammad Nouman Amjad Engineering Research Center of Intelligent Perception and Autonomous Control Faculty of Information TechnologyBeijing University of TechnologyBeijing100124China Faculty of Computer Science and Engineering GIK Institute of Engineering Sciences and TechnologyTopi23460Pakistan Department of Computer Science College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Information Security and Cybersecurity Unit King Khalid UniversityAbhaSaudi Arabia Computer Engineering Department College of Information TechnologyUniversity of Bahrain32038Bahrain School of Engineering Edith Cowan UniversityJoondalup PerthWA6027Australia School of Engineering University of Management and TechnologyLahorePakistan

With the recent developments in the Internet of Things(IoT),the amount of data collected has expanded tremendously,resulting in a higher demand for data storage,computational capacity,and real-time processing *** computing has traditionally played an important role in establishing ***,fog computing has recently emerged as a new field complementing cloud computing due to its enhanced mobility,location awareness,heterogeneity,scalability,low latency,and geographic ***,IoT networks are vulnerable to unwanted assaults because of their open and shared *** a result,various fog computing-based security models that protect IoT networks have been developed.A distributed architecture based on an intrusion detection system(IDS)ensures that a dynamic,scalable IoT environment with the ability to disperse centralized tasks to local fog nodes and which successfully detects advanced malicious threats is *** this study,we examined the time-related aspects of network traffic *** presented an intrusion detection model based on a twolayered bidirectional long short-term memory(Bi-LSTM)with an attention mechanism for traffic data classification verified on the UNSW-NB15 benchmark *** showed that the suggested model outperformed numerous leading-edge Network IDS that used machine learning models in terms of accuracy,precision,recall and F1 score.

关键词： Fog computing intrusion detection bi-LSTM attention mechanism

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Pruning techniques for artificial intelligence networks: a deeper look at their engineering design and bias: the first review of its kind

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Multimedia Tools and Applications 2025年第11期84卷 9591-9665页

作者： Mohanty, Lopamudra Kumar, Ashish Mehta, Vivek Agarwal, Mohit Suri, Jasjit S. School of Computer Science Engineering and Technology Bennett University Gr. Noida India Department of Computer Science ABES Engineering College UP Ghaziabad India Stroke Diagnostic and Monitoring Division AtheroPoint™ RosevilleCA United States

Trained Artificial Intelligence (AI) models are challenging to install on edge devices as they are low in memory and computational power. Pruned AI (PAI) models are therefore needed with minimal degradation in performance. This is the first study of its kind where we have characterized three types of PAI paradigms, namely, weight pruning (WP), channel pruning (CP), and hybrid pruning (HP). Further, the study also analyses the risk of bias (RoB) in PAI models, and explainable PAI (XPAI) networks. PRISMA model was adapted for the selection of the best 145 studies. The review had two hypotheses: (i) PAI had bias and (ii) the order of performance for the three PAI is: HP > CP > WP in terms of performance. Thirty-two AI attributes using each of two experts were categorized into four clusters, namely, architecture, optimization, dataset design, and scientific validation. The RoB was analyzed in these PAI studies using AP(ai)Bias 2.0 (AtheroPoint™, CA, USA), which was based on a scoring-and-ranking strategy. This was then compared against the Butterfly model, composed of radial and regional biases, and analyzed using a Venn diagram in three independent bins based on low-moderate and moderate-high cut-offs. Recommendations were laid out for low-bias studies. PAI studies that lack adequate AI attributes were more inclined to the RoB. It has been observed that 19.64%, 21.42%, and 58.92% studies were in low-bias, moderate-bias, and high-bias bins, respectively. Pruning made inference faster and reduced sparsity. We have observed low participation of the PAI studies in the XPAI framework. The study presented the challenges and five-step recommendations for mitigating bias in PAI models. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Risk assessment

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Image Analysis and Machine Learning in Malaria Parasite Detection: Recent Advances and Future Perspective 4th

Image Analysis and Machine Learning in Malaria Parasite Dete...

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4th International Conference on Smart Systems: Innovations in Computing, SSIC 2023

作者： Aggarwal, Gunjan Goyal, Mayank Kumar Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida India

ISBN: (纸本)9789819736898

Plasmodium parasites cause malaria, a deadly disease that continues to pose a significant global health burden, particularly in resource-limited regions. Detecting and classifying the parasite accurately and promptly are crucial for controlling the progression of the disease. Malaria detection and classification are most frequently performed using a single-class classification approach. Single-class classification approaches include image-based classification, feature-based classification, and machine learning-based classification. Data from multiple classes are used in multi-class classification, which is more complex than single-class classification. These methods are often more extensive than single-class techniques since they can identify and categorize several Plasmodium species and their life stages. Ensemble learning, fuzzy logic-based classification, and deep learning-based classification are some of the common multi-class classification techniques. In addition to their advantages, each approach has its own disadvantages. An in-depth review of existing single-class and multi-class approaches for malaria classification is provided in this paper to gain a better understanding of the different approaches. In the end, this study attempts to present a thorough analysis of the current approaches for classifying malaria. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Contrastive Learning

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Sufficient learning: mining denser high-quality pixel-level labels for edge detection

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Neural Computing and Applications 2025年第14期37卷 8245-8260页

作者： Chen, Xiaodiao Yang, Wenya Wu, Wen Tao, Xiuting Mao, Xiaoyang School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China School of Media and Design Hangzhou Dianzi University Hangzhou310018 China Department of Computer Science and Engineering University of Yamanashi Kofu400-8511 Japan

The success of deep neural networks can largely be attributed to large-scale datasets with accurate annotations. In many practical applications, labels are annotated by multiple annotators, resulting in ambiguous labels. To mitigate the impact of such labels, existing deep edge detectors tend only to exploit sparsely reliable samples with high-confident labels by simple voting mechanism. However, lacking sufficient pixel-level learnable samples would severely degrade detection performance. To combat this issue, this work designs a sufficient learning framework for edge detection (SLED) to sufficiently explore reliable samples for model learning, which consists of three stages: sample selection, label refinement, and retraining. Firstly, we propose a sample selection scheme founded on history prediction, which aims to identify more reliable samples and locate unreliable regions containing potentially reliable samples. Secondly, we design an efficient label refinement strategy based on a graph convolutional network (GCN) to propagate reliable label information to unreliable nodes. This strategy can generate "new training targets" with denser, reliable labels for all images. Finally, we retrain the network with these new targets, obtaining a more robust model. Experimental results on three widely used benchmarks show that the proposed detector outperforms all compared SOTA methods. Our code will be released at https://***/wenya1994/SLED. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Pixels

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A Disease Prediction Framework Based on Predictive Modelling

A Disease Prediction Framework Based on Predictive Modelling

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International Conference on Data Science and Network engineering, ICDSNE 2023

作者： Kaur, Harmohanjeet Shah, Pooja Muhuri, Samya Kumari, Suchi Computer Science & Engineering Department Thapar Institute of Engineering and Technology Punjab India School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Greater Noida India

ISBN: (纸本)9789819967544

The rise of chronic diseases has become a major public health challenge globally. Early prediction and prevention of these diseases can help reduce their prevalence and improve patient outcomes. The proposed disease prediction system, which is based on predictive modeling, may anticipate the user’s illness by using the user’s symptoms as input. The framework evaluates the symptoms taken as input by the user and generates the likelihood of developing the disease. The disease prediction framework based on machine learning (ML) techniques can help in a more accurate diagnosis than conventional methods. In the current manuscript, we have designed a disease prediction methodology using multiple ML techniques. The proposed framework also has the potential to enhance disease surveillance and support public health interventions, including disease management and resource allocation. The accuracy of our approach is shown over the benchmark data sets, which consist of more than .230 diseases. The suggested diagnostic algorithm outputs the disease name that a person might be experiencing based on the symptoms taken into consideration. The proposed framework provides a scalable and effective solution for public health decision-makers to manage chronic diseases and improve patient outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Forecasting

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Glaucoma Detection Approach based on Colour Fundus Pictures 18

Glaucoma Detection Approach based on Colour Fundus Pictures

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Afshan, Roshni Chakraverti, Ashish Kumar Chhabra, Megha Sharda University School of Engineering and Technology Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9789380544519

Glaucoma is a common retinal disorder that has an impact on the optic nerve, resulting in irreversible sight loss if left untreated. Although early detection is crucial for optimal management, manual detection is difficult and needs highly competent ophthalmologists. To overcome this issue, this study Employ Convolutional Neural Networks (CNNs) and further deep learning techniques, to detect glaucoma automatically. The study employs a standardization technique on a dataset of 5000 retinal pictures to ensure that all images are uniform at 256× 256 pixels. Data augmentation techniques such as rescaling, rotation, vertical and horizontal shifts are used consistently to improve dataset variety and model resilience. The MobileNetV3 architecture is used for glaucoma detection, and two optimizers, Stochastic Gradient Descent (SGD) and Adam are used during model training. Pre-processing refines incoming pictures further by turning them to greyscale, adding Gaussian noise, and ensuring pixel values are inside the [0, 255] range. Accuracy, precision, recall, specificity, and F-measure are all part of performance evaluation. Comparative analyses evaluate the suggested model's efficacy, assuring its dependability and quality. The results show that the model is proficient in detecting glaucoma due to its excellent classification accuracy. This work improves automated glaucoma detection, perhaps assisting in early diagnosis and treatment, so protecting patients from irreparable vision loss caused by this 'silent thief of vision.' © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Convolutional neural network Deep learning Glucoma image Machine learning

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