Detection Of Arrhythmia Using Machine Learning(Heart Disease) And ECG

M, Ram Kumar; E, Gokula Krishnan; Dharneeshwar, R; M, Dinep Kumar

doi:10.1109/icacta54488.2022.9752920

Cited by 1 publication

(2 citation statements)

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“…Malaria, an urgent tropical disease caused by Plasmodium parasites transmitted through the bites of infected Anopheles female mosquitoes, remains a significant global health concern [1], [2]. It is characterized by debilitating symptoms such as fever, vomiting, headaches, and fatigue, and can escalate to critical conditions, including coma and fatality [3]. Despite efforts to control its transmission, malaria continues to affect millions worldwide, particularly in endemic regions where the availability of skilled medical professionals and resources is limited [2].…”

Section: Introductionmentioning

confidence: 99%

“…Efficient and precise diagnostic methods are pivotal for effective malaria prevention, control, and treatment. Traditional approaches like microscopic examination of blood smears, while widely employed, have inherent limitations such as timeintensive procedures and subjective interpretations [3], [4]. The diagnostic accuracy may vary between observers, leading to potential misdiagnoses and inappropriate treatments [5].…”

Section: Introductionmentioning

confidence: 99%

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Malaria detection using machine learning

Almakhzoumi,

Bonny,

Al-Shabi

2024

Optics, Photonics, and Digital Technologies for Imaging Applications VIII

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Malaria, a significant global health concern, necessitates precise diagnostic tools for effective management. This study introduces an innovative approach to malaria detection using advanced machinelearning techniques. By harnessing convolutional neural networks (CNNs) and deep learning, the research presents a robust framework for automated malaria detection through microscopic images of red blood cells. The study evaluates three key algorithms-CNN, VGG-16, and Support Vector Machine (SVM)-using a meticulously curated dataset of 27,560 images. Results highlight the VGG-16 model's exceptional accuracy, achieving 98.5%. Transfer learning is pivotal in its success, demonstrating the power of pre-trained models for medical image analysis. This research advances automated disease diagnosis, particularly in resource-limited settings. Future work involves fine-tuning algorithms, exploring ensemble techniques, and enhancing interpretability for broader healthcare applications.

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