Data mining technique for identification of diagnostic biomarker to predict Schizophrenia disorder

GeethaRamani, R.; Sivaselvi, K.

doi:10.1109/iccic.2014.7238525

Cited by 6 publications

(1 citation statement)

References 23 publications

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“…The onset of the symptoms and diagnosis used to be during the second and third life decades and is controversial regarding the sex ratio, which is balanced between genders depending on the methodology, but in clinical groups has higher prevalence in men, as was found in the current Spanish study [ 10 ]. It is characterized by many different symptoms and signs such as thought disorder, delusions, emotional blunting, hallucinations, changes in volition, as well as cognitive deficits [ 11 , 12 , 13 , 14 ]. However, the main phenomenological feature is the variety of symptomatology and lack of a pathognomonic symptom or sign.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Machine Learning Algorithms in the Prediction of Hospitalized Patients with Schizophrenia

Alonso

Marques

Agarwal

et al. 2022

Sensors

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New computational methods have emerged through science and technology to support the diagnosis of mental health disorders. Predictive models developed from machine learning algorithms can identify disorders such as schizophrenia and support clinical decision making. This research aims to compare the performance of machine learning algorithms: Decision Tree, AdaBoost, Random Forest, Naïve Bayes, Support Vector Machine, and k-Nearest Neighbor in the prediction of hospitalized patients with schizophrenia. The data set used in the study contains a total of 11,884 electronic admission records corresponding to 6933 patients with various mental health disorders; these records belong to the acute units of 11 public hospitals in a region of Spain. Of the total, 5968 records correspond to patients diagnosed with schizophrenia (3002 patients) and 5916 records correspond to patients with other mental health disorders (3931 patients). The results recommend Random Forest with the best accuracy of 72.7%. Furthermore, this algorithm presents 79.6%, 72.8%, 72.7%, and 72.7% for AUC, precision, F1-Score, and recall, respectively. The results obtained suggest that the use of machine learning algorithms can classify hospitalized patients with schizophrenia in this population and help in the hospital management of this type of disorder, to reduce the costs associated with hospitalization.

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Section: Introductionmentioning

confidence: 99%

Comparison of Machine Learning Algorithms in the Prediction of Hospitalized Patients with Schizophrenia

Alonso

Marques

Agarwal

et al. 2022

Sensors

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Data Mining Techniques and Algorithms in Psychiatric Health: A Systematic Review

Mehndiratta¹,

S²,

Chaturvedi³

et al. 2021

Biomedical Data Mining for Information Retrieval

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A meta-analysis and systematic review of single vs. multimodal neuroimaging techniques in the classification of psychosis

Porter,

Fei,

Damme

et al. 2023

Mol Psychiatry

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Background Psychotic disorders are characterized by structural and functional abnormalities in brain networks. Neuroimaging techniques map and characterize such abnormalities using unique features (e.g., structural integrity, coactivation). However, it is unclear if a specific method, or a combination of modalities, is particularly effective in identifying differences in brain networks of someone with a psychotic disorder. Methods A systematic meta-analysis evaluated machine learning classification of schizophrenia spectrum disorders in comparison to healthy control participants using various neuroimaging modalities (i.e., T1-weighted imaging (T1), diffusion tensor imaging (DTI), resting state functional connectivity (rs-FC), or some combination (multimodal)). Criteria for manuscript inclusion included whole-brain analyses and cross-validation to provide a complete picture regarding the predictive ability of large-scale brain systems in psychosis. For this meta-analysis, we searched Ovid MEDLINE, PubMed, PsychInfo, Google Scholar, and Web of Science published between inception and March 13th 2023. Prediction results were averaged for studies using the same dataset, but parallel analyses were run that included studies with pooled sample across many datasets. We assessed bias through funnel plot asymmetry. A bivariate regression model determined whether differences in imaging modality, demographics, and preprocessing methods moderated classification. Separate models were run for studies with internal prediction (via cross-validation) and external prediction. Results 93 studies were identified for quantitative review (30 T1, 9 DTI, 40 rs-FC, and 14 multimodal). As a whole, all modalities reliably differentiated those with schizophrenia spectrum disorders from controls (OR = 2.64 (95%CI = 2.33 to 2.95)). However, classification was relatively similar across modalities: no differences were seen across modalities in the classification of independent internal data, and a small advantage was seen for rs-FC studies relative to T1 studies in classification in external datasets. We found large amounts of heterogeneity across results resulting in significant signs of bias in funnel plots and Egger’s tests. Results remained similar, however, when studies were restricted to those with less heterogeneity, with continued small advantages for rs-FC relative to structural measures. Notably, in all cases, no significant differences were seen between multimodal and unimodal approaches, with rs-FC and unimodal studies reporting largely overlapping classification performance. Differences in demographics and analysis or denoising were not associated with changes in classification scores. Conclusions The results of this study suggest that neuroimaging approaches have promise in the classification of psychosis. Interestingly, at present most modalities perform similarly in the classification of psychosis, with slight advantages for rs-FC relative to structural modalities in some specific cases. Notably, results differed substantially across studies, with suggestions of biased effect sizes, particularly highlighting the need for more studies using external prediction and large sample sizes. Adopting more rigorous and systematized standards will add significant value toward understanding and treating this critical population.

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Data mining technique for identification of diagnostic biomarker to predict Schizophrenia disorder

Cited by 6 publications

References 23 publications

Comparison of Machine Learning Algorithms in the Prediction of Hospitalized Patients with Schizophrenia

Comparison of Machine Learning Algorithms in the Prediction of Hospitalized Patients with Schizophrenia

Data Mining Techniques and Algorithms in Psychiatric Health: A Systematic Review

A meta-analysis and systematic review of single vs. multimodal neuroimaging techniques in the classification of psychosis

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