Random Forest Feature Selection, Fusion and Ensemble Strategy: Combining Multiple Morphological MRI Measures to Discriminate among healthy elderly, MCI, cMCI and Alzheimer's disease patients: from the Alzheimer’s disease neuroimaging initiative (ADNI) database

Dimitriadis, Stavros I.; Liparas, Dimitris; Tsolaki, Magda

doi:10.1101/236141

Cited by 7 publications

(13 citation statements)

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“…Two themes were identified in the diagnosis category: (i) predicting the diagnosis of a new patient based on a training dataset of prior diagnoses (e.g. Mohammadi et al ., 2015; Skåtun et al ., 2016; Dimitriadis et al ., 2018); and (ii) differentiating between mental health conditions with similar symptomatology (e.g. Faedda et al ., 2016; Bosl et al ., 2017).…”

Section: Resultsmentioning

confidence: 99%

“…For example, fMRI data have been used with supervised ML to improve the diagnosis of schizophrenia (Skåtun et al ., 2016). Further, MRI data were used with supervised ML to diagnose patients with Alzheimer's disease and cognitive impairment, achieving reasonable accuracy (Dimitriadis et al ., 2018). In addition, supervised ML has also been applied to the diagnosis of mental health conditions with similar symptomatology, for example differentiation of autism spectrum disorders and epilepsy using EEG data (Bosl et al ., 2017).…”

Section: Resultsmentioning

confidence: 99%

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Machine learning in mental health: a scoping review of methods and applications

2019

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BackgroundThis paper aims to synthesise the literature on machine learning (ML) and big data applications for mental health, highlighting current research and applications in practice.MethodsWe employed a scoping review methodology to rapidly map the field of ML in mental health. Eight health and information technology research databases were searched for papers covering this domain. Articles were assessed by two reviewers, and data were extracted on the article's mental health application, ML technique, data type, and study results. Articles were then synthesised via narrative review.ResultsThree hundred papers focusing on the application of ML to mental health were identified. Four main application domains emerged in the literature, including: (i) detection and diagnosis; (ii) prognosis, treatment and support; (iii) public health, and; (iv) research and clinical administration. The most common mental health conditions addressed included depression, schizophrenia, and Alzheimer's disease. ML techniques used included support vector machines, decision trees, neural networks, latent Dirichlet allocation, and clustering.ConclusionsOverall, the application of ML to mental health has demonstrated a range of benefits across the areas of diagnosis, treatment and support, research, and clinical administration. With the majority of studies identified focusing on the detection and diagnosis of mental health conditions, it is evident that there is significant room for the application of ML to other areas of psychology and mental health. The challenges of using ML techniques are discussed, as well as opportunities to improve and advance the field.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Machine learning in mental health: a scoping review of methods and applications

2019

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“…For example, fMRI data has been used with ML to improve the diagnosis of schizophrenia [19]. Further, MRI data was used to diagnose patients with Alzheimer's disease and cognitive impairment, achieving reasonable accuracy [20]. In addition, ML has also been applied to the diagnosis of mental health conditions with similar symptomatology, for example differentiation of autism spectrum disorders and epilepsy using EEG data [21].…”

Section: Detection and Diagnosismentioning

confidence: 99%

“…[18][19][20]; and (ii) differentiating between mental health conditions with similar symptomatology, e.g. [21,22].…”

Section: Detection and Diagnosismentioning

confidence: 99%

“…Further research is also required to ensure that the techniques proposed in a research context can be translated into diagnosis options for the public. Mania NLP [128], NB [128], NN [128] Letters [128] Mild Cognitive Impairment BN [27,69], ensemble learning [27,69], Regression [30], RF [20], Similarity Discriminative Dictionary Learning (SCDDL) algorithm [41], SVM [23] Imaging [20,27,30,41,69], Audio [23] Obsessive Compulsive Disorder NN [129], kNN [129], NB [129], Searchlight Based Feature Extraction (SBFE) [130], SLR algorithm [131], L1-SCCA algorithm [131], SVM [129,132] Imaging [129][130][131][132] Parkinson's Disease SVM [38], RF [38], DT [38], Regression [38] Clinical Assessment [38] Prognosis, Treatment and Support…”

Section: Detection and Diagnosismentioning

confidence: 99%

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Machine learning in mental health: A systematic scoping review of methods and applications

Shatte¹,

Hutchinson²,

Teague³

2018

Preprint

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ObjectiveThis paper aims to synthesise the literature on machine learning (ML) and big data applications for mental health, highlighting current research and applications in practice. Materials and MethodsEight health and information technology research databases were searched using the terms "big data" or "machine learning" and "mental health". Articles were assessed by two reviewers, and data were extracted on the article's mental health application, ML technique, data type and size, and study results. Articles were then synthesised via narrative review. ResultsThree hundred papers focusing on the application of ML to mental health were identified. Four main application domains emerged in the literature, including: (i) detection and diagnosis; (ii) prognosis, treatment and support; (iii) public health; and, (iv) research and clinical administration. The most common mental health conditions addressed included depression, schizophrenia, and Alzheimer's Disease. ML techniques used included support vector machines, decision trees, neural networks, latent dirichlet allocation, and clustering. Discussion and ConclusionOverall, the application of ML to mental health has demonstrated a range of benefits across the areas of diagnosis, treatment and support, research, and clinical administration.With the majority of studies identified focusing on the detection and diagnosis of mental health conditions, it is evident that there is significant room for the application of ML to improve other areas of psychological functioning. The challenges of using ML techniques are discussed, as well as opportunities to improve and advance the field. BACKGROUND AND SIGNIFICANCE

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A novel method for EEG based automated eyes state classification using recurrence plots and machine learning approach

Khosla

Khandnor

Chand

2022

Concurrency and Computation

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Automated eyes-state classification from the EEG signals using nonlinear analysis tools is a new area of research. Based upon the theory of nonlinear analysis, recurrence plots (RPs) and recurrence quantification analysis (RQA) are of greater significance that help in understanding the chaotic and recurrence behavior of the dynamically occurring complex physiological signals. In our study, a novel method is proposed combining the RPs with the machine learning based algorithms for automated classification of EEG signals into eyes-open and eyes-close states. A huge dataset of 109 subjects has been acquired from the PhysioNet database. Each of the six RQA-based measures (recurrence rate, determinism, entropy, laminarity, trapping time, and longest vertical line)has been extracted from 64 EEG channels. Feature selection has been performed using genetic algorithm. The selected features have been averaged and combined to form a six-dimensional input vector which shows statistically significant differences (p < 0.01) between the two states. It is fed to different machine learning based algorithms such as logistic-regression, support vector machine, random forest, k-nearest neighbor, Gaussian naïve Bayes, and adaptive boosting. Logistic regression achieves the highest performance results in terms of accuracy, F1 score, precision, recall, and specificity of 97.27%, 97.17%, 98.26%, 96.36%, and 98.18%, respectively, with the least testing time of the model as 2.52 ms. Therefore, our method might be of greater significance in the development of the practical applications.

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Random Forest Feature Selection, Fusion and Ensemble Strategy: Combining Multiple Morphological MRI Measures to Discriminate among healthy elderly, MCI, cMCI and Alzheimer's disease patients: from the Alzheimer’s disease neuroimaging initiative (ADNI) database

Abstract: Abstract:Background:

Cited by 7 publications

References 61 publications

Machine learning in mental health: a scoping review of methods and applications

Machine learning in mental health: a scoping review of methods and applications

Machine learning in mental health: A systematic scoping review of methods and applications

A novel method for EEG based automated eyes state classification using recurrence plots and machine learning approach

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