A wrapper-based approach for feature selection and classification of major depressive disorder–bipolar disorders

Ergüzel, Türker Tekin; Taş, Cumhur; Çebi, Merve

doi:10.1016/j.compbiomed.2015.06.021

Cited by 63 publications

(30 citation statements)

References 85 publications

(102 reference statements)

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“…These methods are also sensitive to spatially distributed and subtle brain effects that would otherwise be indistinguishable by applying traditional univariate methods that focus on gross differences at the group level [23]. Although ANN methods are used in biomedical studies, AI techniques in psychiatric disorders are still incipient.…”

Section: Discussionmentioning

confidence: 99%

“…In the last years, there has been an upsurge of interest within the neuroscience community in the use of artificial intelligence (AI) methods, including ANN [22] [23]. Moreover, ANN analyses are gaining traction in psychiatric research, providing predictive models for both clinical practice and public health systems.…”

Section: Introductionmentioning

confidence: 99%

“…Advances in technology and data acquisition have simplified the collection and storage of large datasets with long time series, finding increasingly frequent and varied fields of application, including biomedical and data mining areas. In this way, the process of evaluating large volumes of data is an invaluable process M. B. Fonseca et al DOI: 10.4236/nm.2018.94021 217 Neuroscience & Medicineand the recent studies emphasize the use of AI methods with promising results[23]…”

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confidence: 99%

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Bipolar and Schizophrenia Disorders Diagnosis Using Artificial Neural Network

Fonseca¹,

Andrades²,

Bach³

et al. 2018

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Motivation: Bipolar disorder (BD) and schizophrenia (SZ) has a difficult diagnosis, so the main objective of this article is to propose the use of Artificial Neural Networks (ANNs) to classify (diagnose) groups of patients with BD or SZ from a control group using sociodemographic and biochemical variables. Methods: Artificial neural networks are used as classifying tool. The data from this study were obtained from the array collection from Stanley Neuropathology Consortium databank. Inflammatory markers and characteristics of the sampled population were the inputs variables. Results: Our findings suggest that an artificial neural network could be trained with more than 90% accuracy, aiming the classification and diagnosis of bipolar, schizophrenia and control healthy group. Conclusion: Trained ANNs could be used to improve diagnosis in Schizophrenia and Bipolar disorders. upon the activation function [1]. The ANN inputs are multiplied by different weights to generate a predictive response. So, these responses in ANN are widely used for several applications such as classification and pattern recognition [2].This tool is effective modeling non-linear relationships that may be a promising candidate for differentiation for several biological processes [3]. ANN are used in medical field to analysis of sleep disorders, cytopathology and histopathology

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Bipolar and Schizophrenia Disorders Diagnosis Using Artificial Neural Network

Fonseca¹,

Andrades²,

Bach³

et al. 2018

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“…The filter algorithm has a linear time complexity and runs fast enough for many large datasets (Xu et al, 2018). A wrapper utilizes a few heuristic rules to generate a feature subset with a performance evaluation iteratively, and the final feature subset is output if the stop criterion is met (Tekin Erguzel et al, 2015). The strategies of both filters and wrappers may be integrated to generate a hybrid feature selection algorithm (Kumar and Nirmalkumar, 2019;Wu et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Detection and Comparative Analysis of Methylomic Biomarkers of Rheumatoid Arthritis

et al. 2020

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Rheumatoid arthritis (RA) is a common autoimmune disorder influenced by both genetic and environmental factors. To investigate possible contributions of DNA methylation to the etiology of RA with minimum confounding genetic heterogeneity, we investigated genome-wide DNA methylation in disease-discordant monozygotic twin pairs. This study hypothesized that methylomic biomarkers might facilitate accurate RA detection. A comprehensive series of biomarker detection algorithms were utilized to find the best methylomic biomarkers for detecting RA patients using the methylomic data of the peripheral blood samples. The best model achieved 100.00% in accuracy (Acc) with 81 methylomic biomarkers and a 10-fold cross-validation (10FCV) strategy. Some of the methylomic biomarkers were experimentally confirmed to be associated with the onset or development of RA. It is also interesting to observe that many of the detected biomarkers were from chromosome Y, supporting the knowledge that RA has a significant gender discrepancy.

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“…The coupling of ML with MRI, EEG or even blood tests can reveal patterns that allow patients to be divided into different groups (e.g., patients at risk of relapse or patients with active disease). For example, ML is already showing considerable promise for predicting psychotic transition in patients in an at-risk mental state [26], as well as in the field of mood disorders [27].…”

Section: Ml-based Assessmentmentioning

confidence: 99%