The use of deep learning and machine learning (ML) in medical science is increasing, particularly in the visual, audio, and language data fields. We aimed to build a new optimized ensemble model by blending a DNN (deep neural network) model with two ML models for disease prediction using la...
Druggable protein prediction using a multi-canal deep convolutional neural network based on autocovariance method Drug targets must be identified and positioned correctly to research and manufacture new drugs. In this study, rather than using traditional methods for dr... MS Iraji,J Tanha,M Habibine...
it is clear that the cross-domain model proposed in this paper has better prediction results for specific CHD tasks (all-cause death, cardiac function, and Mace occurrence
and the results are presented in this analysis. We identified 44 models, classified as one or more of the following: event prediction (4), spatial (26), ecological niche (28), diagnostic or clinical (6), spread or response (9), and reviews (3). The model parameters...
In general, gene expression microarrays consist of a vast number of genes and very few samples, which represents a critical challenge for disease prediction and diagnosis. This paper develops a two-stage algorithm that integrates feature selection and prediction by extending a type of hetero-associati...
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The cloud will use the encrypted prediction models trained by it to diagnose the diseases without getting privacy information. Then, the hospital will return the prediction results to the patient. Specifically, the contributions of this paper can be summarized as follows. The remainder of this ...
Despite decades of research, the aetiology of pre-eclampsia, particularly of term and postpartum pre-eclampsia, remains poorly defined. Significant advances have been made in the prediction and prevention of preterm pre-eclampsia, which is predicted in early pregnancy through combined screening and is...
This paper contains the following significant points: The present study examines the contributions of different feature selection techniques, filter, wrapper, and evolutionary methods (16 methods) effect on machine–learning algorithms for heart disease prediction. In the subsequent phase, all sixteen feat...
In particular, non-overlapping samples as well as overlapping samples from each data can be used to build a prediction model. Results Study participants All individuals used in the analysis were participants of the Alzheimer’s Disease Neuroimaging Initiative (ADNI)17,18. The overall goal of ADNI...