Prediction of Heart Disease using Random Forest

Lutimath, Nagaraj M.; Sharma, Neha; Byregowda, B K

doi:10.1109/eti4.051663.2021.9619208

Cited by 15 publications

(5 citation statements)

References 7 publications

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“…The generator is one of them, since it is used to create Y domain style images from the X domain, and the generator will create the Y domain image. Restore the image of the X domain [ 36 ]. The discriminator is used to make the image generated by the generator as close to the image of the Y domain style as possible, and the discriminator is used to make the image generated by the generator as close to the original image of the original X domain as possible so that when the image style is transferred, the features of the original image in the original X domain remain.…”

Section: Related Workmentioning

confidence: 99%

Cyclic GAN Model to Classify Breast Cancer Data for Pathological Healthcare Task

Chopra

Junath

Singh

et al. 2022

BioMed Research International

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An algorithm framework based on CycleGAN and an upgraded dual-path network (DPN) is suggested to address the difficulties of uneven staining in pathological pictures and difficulty of discriminating benign from malignant cells. CycleGAN is used for color normalization in pathological pictures to tackle the problem of uneven staining. However, the resultant detection model is ineffective. By overlapping the images, the DPN uses the addition of small convolution, deconvolution, and attention mechanisms to enhance the model’s ability to classify the texture features of pathological images on the BreaKHis dataset. The parameters that are taken into consideration for measuring the accuracy of the proposed model are false-positive rate, false-negative rate, recall, precision, and F 1 score. Several experiments are carried out over the selected parameters, such as making comparisons between benign and malignant classification accuracy under different normalization methods, comparison of accuracy of image level and patient level using different CNN models, correlating the correctness of DPN68-A network with different deep learning models and other classification algorithms at all magnifications. The results thus obtained have proved that the proposed model DPN68-A network can effectively classify the benign and malignant breast cancer pathological images at various magnifications. The proposed model also is able to better assist the pathologists in diagnosing the patients by synthesizing the images of different magnifications in the clinical stage.

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Section: Related Workmentioning

confidence: 99%

Cyclic GAN Model to Classify Breast Cancer Data for Pathological Healthcare Task

Chopra

Junath

Singh

et al. 2022

BioMed Research International

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“…This method uses IoMT to send images captured by iLog glasses to a device at the network's edge. Images are broken up by the edge computing device, and the TensorFlow Object Detection API is used to identify objects in the images [ 66 , 67 ].…”

Section: Discussionmentioning

confidence: 99%

An IoMT‐Based Approach for Real‐Time Monitoring Using Wearable Neuro‐Sensors

Bhatt

Sharma

2023

Journal of Healthcare Engineering

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The Internet of Things (IoT) has demonstrated over the past few decades to be a powerful tool for connecting various medical equipment with in-built sensors and healthcare professionals to deliver superior health services that also reach remote areas. In addition to reducing healthcare costs, increasing access to clinical services, and enhancing operational effectiveness in the healthcare industry, it has also enhanced patient health safety. Recent research has focused on using EEG to assist and comprehend brain changes in rehabilitation facilities. These technologies can spot fluctuations in EEG constraints during treatment, which could result in more effective therapy and better functional outcomes. As a result, we have tried to use an IoT-based system for real-time monitoring of the constraints. Another unknown patient who is suffering from acute ischemic stroke may experience stroke-in-evolution or an early worsening of neurological symptoms, which is frequently associated with poor clinical outcomes. Because of this, managing an acute stroke requires early detection of these indications. The present investigation work will act as a standard reference for academic researchers, medical professionals, and everyone else involved in the use of IoMT. This study aims to anticipate strokes sooner and prevent their consequences by early intervention using an Internet of Things (IoT)-based system. Also, this study proposes usage of wearable equipment that can monitor and analyze brain signals for improved treatment and the prevention of stroke-related complications.

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“…The spindle vibration monitoring system of NC biomedical machine tool generally adopts the classical signal processing method based on stationary process, which is difficult to accurately describe the local characteristics with strong nonstationarity caused by working condition changes or faults. Therefore, the spindle vibration monitoring system of NC biomedical machine tool needs to be able to monitor not only the time domain waveform and frequency domain characteristic quantity of vibration signal, but also the time-frequency characteristic quantity that can reflect the local characteristics of vibration signal [31,32].…”

Section: Software Design Of Monitoring Systemmentioning

confidence: 99%

Mechanical vibration monitoring system for electrocardiogram machine based on Hilbert‐Huang transformations

Zhu

Samori

2022

The Journal of Engineering

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The monitoring of health and the technologies that are related to it are an exciting area of research. The paper proposes a mechanical manufacturing vibration monitoring system that is based on Hilbert-Huang transformation (HHT) feature extraction to monitor the running state of the spindle of a mechanical numerical control (NC) machine tool of an electrocardiogram (ECG) machine. Real-time monitoring of the time-frequency characteristic quantity of the spindle vibration signal for ECG signals has been made possible due to the online empirical mode decomposition (EMD) method, which is used to obtain the time-frequency characteristic quantity of the spindle vibration signal based on HHT. The experiment shows that the frequency doubling characteristic components in the timefrequency distribution are obvious in the time interval without copper rod contact, but they disappear in the time interval during which copper rods are in contact (0.3 1.1 s, 3 4s in the figure). It has been demonstrated that the system is capable of not only accurately monitoring the characteristic quantity in the frequency domain of the vibration signal produced by the NC machine tool spindle, but also of successfully implementing the monitoring of the time-frequency characteristic quantity in real time.

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Prediction of Heart Disease using Random Forest

Cited by 15 publications

References 7 publications

Cyclic GAN Model to Classify Breast Cancer Data for Pathological Healthcare Task

Cyclic GAN Model to Classify Breast Cancer Data for Pathological Healthcare Task

An IoMT‐Based Approach for Real‐Time Monitoring Using Wearable Neuro‐Sensors

Mechanical vibration monitoring system for electrocardiogram machine based on Hilbert‐Huang transformations

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