Disclaimer/Publishers Note: The statements, opinions and data contained in all publications are solely Meanwhile, the Gaussian process classification shows accuracies of 70% in happy against neutral face, 70% in negative against the neutral faces, and 75% when fusing the expressions. M. J. Zaki, J. T. L. Wang, and H. T. T. Toivonen, Data mining in bioinformatics, ACM SIGKDD Explorations Newsletter, vol. You are accessing a machine-readable page. Please note that many of the page functionalities won't work as expected without javascript enabled. Please note that many of the page functionalities won't work as expected without javascript enabled. Hence, machine learning technology has been useful in predicting and diagnosing them for early treatments. paper provides an outlook on future directions of research or possible applications. Hence, this paper will contribute to the state of the art in the form of a systematic literature review concerning the machine learning techniques applied in predicting mental health problems. To conduct the searching and analysis, the topic stated has been explored in the following publishers website. The data samples consist of two populations that remitted bipolar disorder patients. We include a total of 30 research articles in this review after the screening and identification processes. A total of 30 research articles were included in this review paper. 1, 2011. In order to be human-readable, please install an RSS reader. To RB Members are entitled to receive an RB certificate. "Finite Element-Based Machine Learning Model for Predicting the Mechanical Properties of Composite Hydrogels" Applied Sciences 12, no. ACM Computing Surveys, 2022. For LaTeX users we strongly recommend to prepare references with BibTeX. Making the results obtained by these models understandable should be the main priority toward establishing reliable systems. Random forest is being used in this paper because it has lower error rates compared with other methods. From the obtained results, Sumathi and Poorna showed that multilayer perceptrons can achieve an accuracy of 78%, which is the highest accuracy among machine learning models [49]. The authors use a sample of 441 trauma-exposed subjects as the training data set and 211 trauma-exposed subjects as the new testing data set. [30], Hilbert et al. 6, pp. https://doi.org/10.3390/technologies9010002, Subscribe to receive issue release notifications and newsletters from MDPI journals, You can make submissions to other journals. 11, pp. According to the results provided by the authors, they usually present the accuracy as the performance measurement level for the machine learning models in predicting the mental health problems. A. J. Xu, M. A. Flannery, Y. Gao, and Y. Wu, Machine learning for mental health detection, 2019, https://digitalcommons.wpi.edu/mqp-all/6732/. Currently, there are only two classes of approved drugs to treat AD, including inhibitors to cholinesterase enzyme and antagonists to, This is an open access article distributed under the, Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. Based on the experimental observation of BG-COL composite hydrogels with scanning electron microscope, 2000 microstructural images with randomly distributed BG particles were created. A machine learning algorithm known as gradient-boosted decision trees has been built and applied due to its capability in handling the nonlinear interactions among categorical and continuous features with various distributions. the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. [44], Marmar et al. Moreover, some studies are conducted by using a partial large size of the data set, which is above 100 subjects. In Stage 1, reviewers assess study proposals before data is collected. Accuracy of machine learning techniques in predicting the mental health problems among children. 2Ramachandram D, Taylor G W. Deep multimodal learning: A survey on recent advances and trends[J]. The physiological symptoms that are caused by panic disorder include a racing heart, sweating, and dizziness. [37], Akinci et al. 20, no. You seem to have javascript disabled. The neuro-fuzzy system can obtain the highest accuracy with a score of 84.3% followed by random forest with an accuracy of 80.83%. In short, data mining is a crucial technique in the role of computer science. Is a gap in knowledge identified? The unsupervised learning is in contrast to the supervised learning, which tries to make sense of data in itself. A. Ahmed, R. Sultana, M. T. R. Ullas, M. Begom, M. M. I. Rahi, and M. A. Alam, A machine learning approach to detect depression and anxiety using supervised learning, in Proceedings of the 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), Gold Coast, Australia, 2019. The aim is to provide a snapshot of some of the most exciting work Jaiswal, A.; Babu, A.R. 44, no. [39]. Research in the field of mental health has increased recently and contributed to the information and publications about different features of mental health, which can be applied in a wide range of problems [10]. Do they properly show the data? In classifying the depression and anxiety cases with machine learning models, the research shows a better result in terms of accuracy for the studies conducted. A total of 25 variables from the data set are selected to make a better prediction outcome. The performance of the machine learning models will vary depending on the data samples obtained and the features of the data. P. D. McGorry, A. R. Yung, L. J. Phillips et al., Randomized controlled trial of interventions designed to reduce the risk of progression to first-episode psychosis in a clinical sample with subthreshold symptoms, Archives of General Psychiatry, vol. A. M. Chekroud, R. J. Zotti, Z. Shehzad et al., Cross-trial prediction of treatment outcome in depression: a machine learning approach, The Lancet Psychiatry, vol. The primary symptom of depression is an interference of the mood, which is usually severe sadness. 10341043, 2016. Based on the results obtained, using the random forest with conditional inference has shown the highest accuracy of 77.25% compared with the LASSO with an accuracy percentage of 74.88% and logistic regression with an accuracy percentage of 75.36%. The aim is to provide a snapshot of some of the most exciting work Visit our dedicated information section to learn more about MDPI. The support vector machine showed a great result and was able to obtain an accuracy of 95% for anxiety and 95.8% for depression. interesting to authors, or important in this field. For example, structural neuroimaging research projects are usually carried out in subjects who already had mental health illness. Should the reviewer be unable to provide a report when invited, they are expected to suggest alternative potential reviewers (the proposed candidates must meet the reviewers requirements from. First of all, this review paper begins with the planning phase where the research questions or objectives are investigated and determined. G. Akinci, E. Polat, and O. M. Koak, A video based eye detection system for bipolar disorder diagnosis, in Proceedings of the 2012 20th Signal Processing and Communications Applications Conference (SIU), Mugla, Turkey, April 2012. For Powerful algorithms and more advanced neural networks, decision trees, gradient boosting, and others were introduced and applied to solve more complicated medical diagnosis problems. Next, the naive Bayes and multilayer perceptron achieved the accuracy of 79.6% and 77.8%, respectively. This type of With a mean F1-score of 0.73, random forest outperforms all the baseline algorithms. Then, we categorize the collected research articles based on the mental health problems such as schizophrenia, bipolar disorder, anxiety and depression, posttraumatic stress disorder, and mental health problems among children. Unsupervised learning is particularly valuable in helping to identify the structure of the data automatically through learning inherent from input data when the data set is unlabelled. Mental illness is usually diagnosed based on the individual self-report that requires questionnaires designed for the detection of the specific patterns of feeling or social interactions [2]. interesting to authors, or important in this field. 1996-2022 MDPI (Basel, Switzerland) unless otherwise stated. MultiModal Machine Learning (MMML) J. Mouro-Miranda, J. R. Almeida, S. Hassel et al., Pattern recognition analyses of brain activation elicited by happy and neutral faces in unipolar and bipolar depression, Bipolar Disorders, vol. The performance on the machine learning algorithms or techniques that are used by the researchers is being evaluated by identifying the accuracy, sensitivity, specificity, or area under the ROC curve (AUC). Feature Generally, the diagnosis will begin with a specific interview that is filled with questions about symptoms, medical history, and physical examination. All articles published by MDPI are made immediately available worldwide under an open access license. In this approach, the data have a larger volume where the deep learning algorithms can be useful and applied. 2022, 12, 10835. [22] and Srinivasagopalan et al. Although machine learning can show the researchers about the prediction on mental health, there is still a lack of testing being applied in real life due to several reasons. 17351780, 1997. 1, 2017. This paper presents a recent systematic review of machine learning approaches in predicting mental health problems. [41], Leightley et al. The authors used a deep autoencoder and can produce different values and patterns of neuroanatomical deviations. See further details. Supervised learning is excellent at classification and regression problems. [41], and Marmar et al. This review paper will follow the standard PRISMA protocol, which stands for Preferred Reporting Items for Systematic Reviews and Meta-Analyses. 11, 2019. The reviewers are eligible to be considered for the Outstanding Reviewer Awards. Soil moisture is an important parameter affecting environmental processes such as hydrology, ecology, and climate. ; Banerjee, D.; Makedon, F. A Survey on Contrastive Self-Supervised Learning. In order to be human-readable, please install an RSS reader. the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, MDPI and/or Excellent reviewers may be promoted to Reviewer Board Members (subject to approval by the Editor-in-Chief). progress in the field that systematically reviews the most exciting advances in scientific literature. Another research about machine learning approaches in PTSD prediction is conducted by Papini et al. Decisions on revisions, acceptance, or rejections must always be well justified. Machine learning is an application of artificial intelligence (AI), which implements systems with the capability to learn and improve from experience without being explicitly programmed. Many different techniques and algorithms had been introduced and proposed to test and solve the mental health problems. Machine learning models need to have a life-long framework as it can help preventing the catastrophic forgetting [61]. [41] managed to reach an AUC of 0.89 with random forest. In a paper conducted by Holzinger et al., the authors encouraged an innovative and interactive explainable approach called counterfactual graphs for the beneficial future interaction between humans and artificial intelligence [59]. Feature Papers represent the most advanced research with significant potential for high impact in the field. In this study, a finite element (FE)-based machine learning model was developed to predict the mechanical properties of bioglass (BG)-collagen (COL) composite hydrogels. [30], Sau and Bhakta [31], and Hilbert et al. Its comforting. In the research paper by Sumathi and Poorna, the authors have predicted mental health problems among children by various machine learning approaches [49]. Surveys show that delays in seeking professional treatment for an anxiety disorder are widespread [16]. * Please note that you can only see and apply to manuscripts in journals where you are a member of the Reviewers Board or a Volunteer Reviewer. Bipolar disorder is another mental disorder identified by the episode of mania and depression. This is because random forest and support vector machine at most of the time are able to provide an excellent performance in terms of the accuracy For example, Greenstein et al. It is undeniable that machine learning models such as random forest and support vector machine have been the most popular choice to be applied in the experiments. have researched on the audio recordings from the war veterans and compared those with the speech elements of clinicians and patients to predict the PTSD [46]. Moreover, machine learning models can also be affected by preprocessing activities such as data cleaning and parameter tuning in order to achieve optimal results. Does it include an excessive number of self-citations? 10, pp. Besides that, the performances of the machine learning model will be included in this paper to show the efficiency of the machine learning approaches within the mental health field. Aflaj (plural of falaj) are tunnels or trenches built to deliver groundwater from its source to the point of consumption. several techniques or approaches, or a comprehensive review paper with concise and precise updates on the latest [42], Conrad et al. 3, pp. Most of the machine learning algorithms show promising levels of performance in predicting schizophrenia patients and healthy controls. Domy jednorodzinne w zabudowie wolnostojcej ok. 140m, Domy jednorodzinne w zabudowie szeregowej parterowe ok 114m. According to the paper by Greenstein et al., classification of childhood-onset schizophrenia has been performed [20]. Therefore, we review the current state-of-the-a MDPI journals operate single- or double-blind peer review (https://www.mdpi.com/editorial_process). There are also 2 articles that predict the mental health problems among children with various machine learning approaches [49, 50]. Besides that, the queries such as Machine Learning Algorithms in Mental Health, Psychiatric Medical with Machine Learning Techniques, and Machine Learning in Predicting Mental Health Problems have been used on these sites. [27] present that gradient boosting achieves the accuracy of 64.6%. Because my aim was to locate the best algorithm to use. The complexity of the data sets collected can be solved rapidly and swiftly through data mining. The World Health Organization (WHO) reports the region-wise status of different barriers in diagnosing mental health problems and encourages researchers to be equipped with the scientific knowledge to address the issue of mental health [9]. progress in the field that systematically reviews the most exciting advances in scientific literature. 9, no. See further details. Besides that, Rangaprakash et al. 803816, 2019. Resting-state functional connectivity could be used as a spot of functional dysregulation in specific networks that are affected in schizophrenia. 3Baltruaitis T, Ahuja C, Morency L P. Multimodal machine learning: A survey and taxonomy[J]. Mental illness can affect the cognition, emotion, and behaviour among the people. [28], Katsis et al. 130 Executive Board, Global burden of mental disorders and the need for a comprehensive, coordinated response from health and social workers at the country level, 2012, https://apps.who.int/iris/handle/10665/23741. Furthermore, this review paper investigated 2 research papers from each year of 2012, 2013, 2018, and 2020. They obtained the highest AUC by using random forest, which is 0.739, followed by support vector machine, which is 0.736 [50]. Additionally, Rangaprakash et al. The authors have collected and gathered speech samples from warzone-exposed veterans. Shokrollahi, Y.; Dong, P.; Gamage, P.T. The Feature Paper can be either an original research article, a substantial novel research study that often involves The scope of mental health problems is divided into five types of problems, namely, schizophrenia, anxiety and depression, bipolar disorder, posttraumatic stress disorder (PTSD), and mental health problems among children. Online learning generally has a lot of opportunities available but this time of crisis will allow online learning to boom as most academic institutions have switched to this model. All articles published by MDPI are made immediately available worldwide under an open access license. T. I. Mueller, A. C. Leon, M. B. Keller et al., Recurrence after recovery from major depressive disorder during 15 years of observational follow-up, American Journal of Psychiatry, vol. The cognitive symptoms such as slow thinking, suicidal thoughts, and guilt might occur among the patients. Hence, the convolutional neural network can be a helpful model to assist psychologists and counsellors for making the treatments efficient. You seem to have javascript disabled. In this research conducted by them, features including age, educational qualification, marital status, job profile, type of family, duration of service, existence or nonexistence of heart disease, body mass index, hypertension, and diabetes have been selected to predict the outcome. Not only that, challenges and limitations faced by the researchers need to be managed with proper care to achieve satisfactory results that could improve the clinical practice and decision-making. The authors use a random forest method to calculate the probability of mental disorder. prior to publication. Find support for a specific problem in the support section of our website. The innate immunity acts as the first line of po to, by dostosowa serwis do potrzeb uytkownikw, i w celach statystycznych. However, the full-text articles or papers that do not meet the appropriate conditions will be excluded from the review paper for a reason. published in the various research areas of the journal. https://doi.org/10.3390/technologies9010002, Jaiswal A, Babu AR, Zadeh MZ, Banerjee D, Makedon F. A Survey on Contrastive Self-Supervised Learning.
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