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Automated Classification of Cardiac Arrhythmia using Short-Duration ECG Signals and Machine Learning.
Biomed Phys Eng Express
January 2025
Electronics and Communication Engineering, Rajiv Gandhi University, Rono Hills, Doimukh, ITANAGAR, Itanagar, Arunachal Pradesh, 791112, INDIA.
Accurate detection of cardiac arrhythmias is crucial for preventing premature deaths. The current study employs a dual-stage Discrete Wavelet Transform (DWT) and a median filter to eliminate noise from ECG signals. Subsequently, ECG signals are segmented, and QRS regions are extracted for further preprocessing.
View Article and Find Full Text PDFIL-33, a neutrophil extracellular trap-related gene involved in the progression of diabetic kidney disease.
Inflamm Res
January 2025
Department of Nephrology, First Affiliated Hospital of Naval Medical University, Shanghai Changhai Hospital, Shanghai, China.
Background: Chronic inflammation is well recognized as a key factor related to renal function deterioration in patients with diabetic kidney disease (DKD). Neutrophil extracellular traps (NETs) play an important role in amplifying inflammation. With respect to NET-related genes, the aim of this study was to explore the mechanism of DKD progression and therefore identify potential intervention targets.
View Article and Find Full Text PDFBoosting skin cancer diagnosis accuracy with ensemble approach.
Sci Rep
January 2025
School of Information and Electronic Engineering and Zhejiang Key Laboratory of Biomedical Intelligent Computing Technology, Zhejiang University of Science and Technology, No. 318, Hangzhou, Zhejiang, China.
Skin cancer is common and deadly, hence a correct diagnosis at an early age is essential. Effective therapy depends on precise classification of the several skin cancer forms, each with special traits. Because dermoscopy and other sophisticated imaging methods produce detailed lesion images, early detection has been enhanced.
View Article and Find Full Text PDFAn Intelligent System for Classifying Patient Complaints Using Machine Learning and Natural Language Processing: Development and Validation Study.
J Med Internet Res
January 2025
Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center For Child Health, Hang Zhou, China.
Background: Accurate classification of patient complaints is crucial for enhancing patient satisfaction management in health care settings. Traditional manual methods for categorizing complaints often lack efficiency and precision. Thus, there is a growing demand for advanced and automated approaches to streamline the classification process.
View Article and Find Full Text PDFSVMVGGNet-16: A Novel Machine and Deep Learning Based Approaches for Lung Cancer Detection Using Combined SVM and VGGNet-16.
Curr Med Imaging
January 2025
School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan611731, China.
Background And Objective: Lung cancer remains a leading cause of cancer-related mortality worldwide, necessitating early and accurate detection methods. Our study aims to enhance lung cancer detection by integrating VGGNet-16 form of Convolutional Neural Networks (CNNs) and Support Vector Machines (SVM) into a hybrid model (SVMVGGNet-16), leveraging the strengths of both models for high accuracy and reliability in classifying lung cancer types in different 4 classes such as adenocarcinoma (ADC), large cell carcinoma (LCC), Normal, and squamous cell carcinoma (SCC).
Methods: Using the LIDC-IDRI dataset, we pre-processed images with a median filter and histogram equalization, segmented lung tumors through thresholding and edge detection, and extracted geometric features such as area, perimeter, eccentricity, compactness, and circularity.
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