Recursion Enhanced Random Forest With an Improved Linear Model (RERF-ILM) for Heart Disease Detection on the Internet of Medical Things Platform

Guo, Chunyan; Zhang, Jiabing; Liu, Yang; Xie, Yaying; Han, Zhiqiang; Yu, Jianshe

doi:10.1109/access.2020.2981159

Cited by 72 publications

(27 citation statements)

References 22 publications

(23 reference statements)

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“…Machine learning and deep learning have been widely used to identify, detect, predict, and forecast several types of cardiovascular disease. Some previous studies explored cardiology diseases, including heart disease detection [78], prediction of heart failure [79], diagnosis of atrial fibrillation [80], and cardiovascular mortality risk [81]. In addition to cardiovascular disease, endocrine system disorders, especially diabetes mellitus, were also widely discussed.…”

Section: Miscellaneous Diseasesmentioning

confidence: 99%

Machine learning in handling disease outbreaks: a comprehensive review

Riswantini

Nugraheni

2022

Bulletin EEI

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The changes in the global environment have made impact on the evolution of infectious diseases, virus mutations, or new diseases which are challenging to be tackled with new technological advances. This work aims to identify and analyze previous studies on machine learning applications in handling disease outbreaks. Bibliometric analysis was conducted on 3,447 scientific articles selected from the Scopus database. Further, latent dirichlet analysis (LDA) method was applied to identify the topic hotspots in attempting to deepen the analysis. The LDA results identified twelve topic hotspots that can be classified into three themes: COVID-19 disease, miscellaneous diseases, and public opinion on disease outbreaks for discussion. The study reveals that the scientific structure of this domain is dominated by machine learning research on COVID-19 diseases and miscellaneous diseases caused by pathogens or some genetic factors. A huge amount of multimodal medical data was used by previous studies for prediction, forecasting, classification, or screening purposes to resolve many problems of diseases, including epidemiological surveillance, diagnosis, treatment, health monitoring, epidemic management, viral infection, and pathogenesis. Public opinions toward new diseases are also an interesting topic in addition to the public perceptions in response to the health protocol and policies.

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Section: Miscellaneous Diseasesmentioning

confidence: 99%

Machine learning in handling disease outbreaks: a comprehensive review

Riswantini

Nugraheni

2022

Bulletin EEI

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“…By utilizing this framework, they have accomplished 3.30% preferable precision over the ordinary SVM algorithmswhich are available prior [14].…”

Section: Literature Surveymentioning

confidence: 99%

A Survey on Cardiovascular Prediction using Variant Machine learning

Chandrika¹,

Madhavi²,

Sindhuja³

et al. 2021

E3S Web Conf.

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Prediction of a cardiovascular diseases has always a tedious challenge for doctors and medical practitioners. Most of the practitioners and hospital staff offers expensive medication, care and surgeries to treat the cardiovascular patients. At early-stage of prediction of heart-oriented problems will be giving a chance of survival by taking necessary precautions. Over the years there are different types of methodologies were proposed to predict the cardiovascular diseases one of the best methodologies is a Machine learning approach. These years many scientific advancements take place in the Artificial Intelligence, Machine learning, and Deep learning which gives an extra push up to help and implement the path in the field of medical image processing and medical data analysis. By using the enormous dataset from various medical experts used to help the researchers to predict the coronary problems prior to happening. Many researchers have tried and implemented different machine learning algorithms to automate the prediction analysis using the enormous number of datasets. There are numerous algorithms and procedures to predict the cardiovascular diseases and accessible to be specific Classification methods including Artificial Neural Networks (AI), Decision tree (DT), Support vector machine (SVM), Genetic algorithm (GA), Neural network (NN), Naive Bayes (NB) and Clustering algorithms like K-NN. A few examinations have been done for creating expectation models utilizing singular procedures and additionally concatenating at least two strategies. This paper gives a speedy and simple survey and knowledge of approachable prediction models using different researchers work from 2004 to 2019. The examination indicates the precision of individual experiments done by various researchers.

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“…Internet of Medical Things (IoMT) is a medical paradigm that allows for integration of modern technologies in the existing healthcare system [ 9 ]. The algorithms developed as a part of the presented research can be made available to health professionals using IoMT [ 10 ]. Models obtained using the described methodology can be integrated inside a pipeline system in which an X-ray image will automatically be processed using the developed models, and the predicted class of the patient whose image has been obtained will immediately be delivered to the medical professional examining the X-ray.…”

Section: Introductionmentioning

confidence: 99%

Automatic Evaluation of the Lung Condition of COVID-19 Patients Using X-ray Images and Convolutional Neural Networks

Lorencin

Šegota

Anđelić

et al. 2021

JPM

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COVID-19 represents one of the greatest challenges in modern history. Its impact is most noticeable in the health care system, mostly due to the accelerated and increased influx of patients with a more severe clinical picture. These facts are increasing the pressure on health systems. For this reason, the aim is to automate the process of diagnosis and treatment. The research presented in this article conducted an examination of the possibility of classifying the clinical picture of a patient using X-ray images and convolutional neural networks. The research was conducted on the dataset of 185 images that consists of four classes. Due to a lower amount of images, a data augmentation procedure was performed. In order to define the CNN architecture with highest classification performances, multiple CNNs were designed. Results show that the best classification performances can be achieved if ResNet152 is used. This CNN has achieved AUCmacro¯ and AUCmicro¯ up to 0.94, suggesting the possibility of applying CNN to the classification of the clinical picture of COVID-19 patients using an X-ray image of the lungs. When higher layers are frozen during the training procedure, higher AUCmacro¯ and AUCmicro¯ values are achieved. If ResNet152 is utilized, AUCmacro¯ and AUCmicro¯ values up to 0.96 are achieved if all layers except the last 12 are frozen during the training procedure.

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Recursion Enhanced Random Forest With an Improved Linear Model (RERF-ILM) for Heart Disease Detection on the Internet of Medical Things Platform

Cited by 72 publications

References 22 publications

Machine learning in handling disease outbreaks: a comprehensive review

Machine learning in handling disease outbreaks: a comprehensive review

A Survey on Cardiovascular Prediction using Variant Machine learning

Automatic Evaluation of the Lung Condition of COVID-19 Patients Using X-ray Images and Convolutional Neural Networks

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