A novel recommender system based on FFT with machine learning for predicting and identifying heart diseases

Narayan, Subhashini; Sathiyamoorthy, E.

doi:10.1007/s00521-018-3662-3

Cited by 31 publications

(7 citation statements)

References 32 publications

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“…is could be reduced by using tools such as machine learning to classify ERC from the initial stages. Although the application of machine learning in healthcare and other areas is favorable, the field of kidney disease has not yet exploited its full potential [16][17][18][19][20][21][22][23][24][25].…”

Section: Assessment Of the Situationmentioning

confidence: 99%

[Retracted] Implementation of Machine Learning Models for the Prevention of Kidney Diseases (CKD) or Their Derivatives

Alhamazani

Alshudukhi

Aljaloud

et al. 2021

Computational Intelligence and Neuroscience

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Chronic kidney disease (CKD) is a global health issue with a high rate of morbidity and mortality and a high rate of disease progression. Because there are no visible symptoms in the early stages of CKD, patients frequently go unnoticed. The early detection of CKD allows patients to receive timely treatment, slowing the disease’s progression. Due to its rapid recognition performance and accuracy, machine learning models can effectively assist physicians in achieving this goal. We propose a machine learning methodology for the CKD diagnosis in this paper. This information was completely anonymized. As a reference, the CRISP-DM® model (Cross industry standard process for data mining) was used. The data were processed in its entirety in the cloud on the Azure platform, where the sample data was unbalanced. Then the processes for exploration and analysis were carried out. According to what we have learned, the data were balanced using the SMOTE technique. Four matching algorithms were used after the data balancing was completed successfully. Artificial intelligence (AI) (logistic regression, decision forest, neural network, and jungle of decisions). The decision forest outperformed the other machine learning models with a score of 92%, indicating that the approach used in this study provides a good baseline for solutions in the production.

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Section: Assessment Of the Situationmentioning

confidence: 99%

[Retracted] Implementation of Machine Learning Models for the Prevention of Kidney Diseases (CKD) or Their Derivatives

Alhamazani

Alshudukhi

Aljaloud

et al. 2021

Computational Intelligence and Neuroscience

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“…12 13 Table 2 includes the characteristics of all included studies. 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87…”

Section: Resultsmentioning

confidence: 99%

Diversity in Machine Learning: A Systematic Review of Text-Based Diagnostic Applications

2022

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Objective As the storage of clinical data has transitioned into electronic formats, medical informatics has become increasingly relevant in providing diagnostic aid. The purpose of this review is to evaluate machine learning models that use text data for diagnosis and to assess the diversity of the included study populations. Methods We conducted a systematic literature review on three public databases. Two authors reviewed every abstract for inclusion. Articles were included if they used or developed machine learning algorithms to aid in diagnosis. Articles focusing on imaging informatics were excluded. Results From 2,260 identified papers, we included 78. Of the machine learning models used, neural networks were relied upon most frequently (44.9%). Studies had a median population of 661.5 patients, and diseases and disorders of 10 different body systems were studied. Of the 35.9% (N = 28) of papers that included race data, 57.1% (N = 16) of study populations were majority White, 14.3% were majority Asian, and 7.1% were majority Black. In 75% (N = 21) of papers, White was the largest racial group represented. Of the papers included, 43.6% (N = 34) included the sex ratio of the patient population. Discussion With the power to build robust algorithms supported by massive quantities of clinical data, machine learning is shaping the future of diagnostics. Limitations of the underlying data create potential biases, especially if patient demographics are unknown or not included in the training. Conclusion As the movement toward clinical reliance on machine learning accelerates, both recording demographic information and using diverse training sets should be emphasized. Extrapolating algorithms to demographics beyond the original study population leaves large gaps for potential biases.

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“…In the proposed iCovidCare model, we apply the well-known max-min normalization technique. This technique helps to convert and rescale the numerical values of the COVID-19 dataset within the range of [0, 1] [30] . For an instant, in the COVID-19 dataset, one of the feature variables is age, whose values vary from 0100.…”

Section: Icovidcare Model and Disease Prediction Methodologymentioning

confidence: 99%

iCovidCare: Intelligent health monitoring framework for COVID-19 using ensemble random forest in edge networks

Adhikari

Munusamy

2021

Internet of Things

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A novel recommender system based on FFT with machine learning for predicting and identifying heart diseases

Cited by 31 publications

References 32 publications

[Retracted] Implementation of Machine Learning Models for the Prevention of Kidney Diseases (CKD) or Their Derivatives

[Retracted] Implementation of Machine Learning Models for the Prevention of Kidney Diseases (CKD) or Their Derivatives

Diversity in Machine Learning: A Systematic Review of Text-Based Diagnostic Applications

iCovidCare: Intelligent health monitoring framework for COVID-19 using ensemble random forest in edge networks

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