Heart murmur detection based on wavelet transformation and a synergy between artificial neural network and modified neighbor annealing methods

Eslamizadeh, Gholamhossein; Barati, Ramin

doi:10.1016/j.artmed.2017.05.005

Cited by 52 publications

(31 citation statements)

References 20 publications

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“…Accuracy and consistency of current more traditional auscultation techniques are sub‐optimal , and as low as 20% correct identification of cardiac abnormality among clinicians has been reported . Not only do DS carry the potential to advance the teaching abilities of medical faculties , they also possess the capability to remove the user‐dependant error with the development of artificial neural networks and the amalgamation of these technologies to create an automated diagnostic program .…”

Section: Future Of Digital Stethoscope Usementioning

confidence: 99%

Digital stethoscopes in paediatric medicine

et al. 2019

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Aim: To explore, synthesise and discuss currently available digital stethoscopes (DS) and the evidence for their use in paediatric medicine.Methods: Systematic review and narrative synthesis of digital stethoscope use in paediatrics following searches of OVID Medline, Embase, Scopus, PubMed and Google Scholar databases.Results: Six digital stethoscope makes were identified to have been used in paediatric focused studies so far. A total of 25 studies of DS use in paediatrics were included. We discuss the use of digital stethoscope technology in current paediatric medicine, comment on the technical properties of the available devices, the effectiveness and limitations of this technology, and potential uses in the fields of paediatrics and neonatology, from telemedicine to computer-aided diagnostics. Conclusion: Further validation and testing of available DS devices is required. Comparisonstudies between different types of DS would be useful in identifying strengths and flaws of each DS as well as identifying clinical situations for which each may be most appropriately suited.

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Section: Future Of Digital Stethoscope Usementioning

confidence: 99%

Digital stethoscopes in paediatric medicine

et al. 2019

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“…The mean of the total variations between the actual labelled output and the predicted output is measured as Mean Square Error 14 .…”

Section: Mean Square Errormentioning

confidence: 99%

Construction of Feed Forward MultiLayer Perceptron Model For Genetic Dataset in Leishmaniasis Using Cognitive Computing

Mahalingam

Kabra

Singh

2018

Preprint

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Leishmaniasis is an endemic parasitic disease, predominantly found in the poor locality of Africa, Asia and Latin America. It is associated with malnutrition, weak immune system of people and their housing locality. At present, it is diagnosed by microscopic identification, molecular and biochemical characterisation or serum analysis for parasitic compounds. In this study, we present a new approach for diagnosing Leishmaniasis using cognitive computing. The Genetic datasets of leishmaniasis are collected from Gene Expression Omnibus database and it's then processed. The algorithm for training and developing a model, based on the data is prepared and coded using python. The algorithm and their corresponding datasets are integrated using TensorFlow dataframe. A feed forward Artificial Neural Network trained model with multi-layer perceptron is developed as a diagnosing model for Leishmaniasis, using genetic dataset. It is developed using recurrent neural network. The cognitive model of the trained network is interpreted using the maps and mathematical formula of the influencing parameters. The credit of the system is measured using the accuracy, loss and error of the system. This integrated system of the leishmaniasis genetic dataset and neural network proved to be the good choice for diagnosis with higher accuracy and lower error. Through this approach, all records of the data are effectively incorporated into the system. The experimental results of feed forward multilayer perceptron model after normalization; mean square error (219.84), loss function (1.94) and accuracy (85.71%) of the model, shows good fit of model with the process and it could possibly serve as a better solution for diagnosing Leishmaniasis in future, using genetic datasets.The code is available in Github repository: https://github.com/shailzasingh/Machine-Learning-code-for-analyzing-genetic-dataset-in-Leishmaniasis

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“…To reduce the death rates caused by chronic diseases (CDs), early detection and effective treatments are the only solutions [11]. Therefore, most medical scientists are attracted to the new technologies of predictive models in disease forecasting [12]. These new advancements in medical care have been expanding the accessibility of electronic data and opening new doors for decision support and productivity improvements [13].…”

Section: Introductionmentioning

confidence: 99%

Applications of Machine Learning Predictive Models in the Chronic Disease Diagnosis

Battineni

Sagaro

Chinatalapudi

et al. 2020

JPM

240

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This paper reviews applications of machine learning (ML) predictive models in the diagnosis of chronic diseases. Chronic diseases (CDs) are responsible for a major portion of global health costs. Patients who suffer from these diseases need lifelong treatment. Nowadays, predictive models are frequently applied in the diagnosis and forecasting of these diseases. In this study, we reviewed the state-of-the-art approaches that encompass ML models in the primary diagnosis of CD. This analysis covers 453 papers published between 2015 and 2019, and our document search was conducted from PubMed (Medline), and Cumulative Index to Nursing and Allied Health Literature (CINAHL) libraries. Ultimately, 22 studies were selected to present all modeling methods in a precise way that explains CD diagnosis and usage models of individual pathologies with associated strengths and limitations. Our outcomes suggest that there are no standard methods to determine the best approach in real-time clinical practice since each method has its advantages and disadvantages. Among the methods considered, support vector machines (SVM), logistic regression (LR), clustering were the most commonly used. These models are highly applicable in classification, and diagnosis of CD and are expected to become more important in medical practice in the near future.

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Heart murmur detection based on wavelet transformation and a synergy between artificial neural network and modified neighbor annealing methods

Cited by 52 publications

References 20 publications

Digital stethoscopes in paediatric medicine

Digital stethoscopes in paediatric medicine

Construction of Feed Forward MultiLayer Perceptron Model For Genetic Dataset in Leishmaniasis Using Cognitive Computing

Applications of Machine Learning Predictive Models in the Chronic Disease Diagnosis

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