The physiological symptoms that are caused by panic disorder include a racing heart, sweating, and dizziness. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018. Studies of whether an early diagnosis of such symptoms and intervention could improve the outcomes are still in progress [12]. progress in the field that systematically reviews the most exciting advances in scientific literature. However, there is still limited exploration in the use of deep learning algorithms for mental health. The model can achieve an accuracy of 73.6%; meanwhile, the support vector machine obtains an accuracy of 68.1%. The work explains commonly used pretext tasks in a contrastive learning setup, followed by different architectures that have been proposed so far. 37, no. Furthermore, this review paper investigated 2 research papers from each year of 2012, 2013, 2018, and 2020. Leightley et al. 5, no. Editors select a small number of articles recently published in the journal that they believe will be particularly Feature Papers are submitted upon individual invitation or recommendation by the scientific editors and undergo peer review prior to publication. 15, no. To 14, no. applied the deep belief network to interpret features from neuromorphometry data that consist of 83 healthy controls and 143 schizophrenia patients [25]. 1, 2016. 38, no. [23]. The research articles were divided and categorized based on the mental health problems such as schizophrenia, bipolar disorder, anxiety and depression, posttraumatic stress disorder, and mental health problems among children. Mania is known by irritability, increased in energy, and decreased need for sleep. 21, no. No special Is the manuscript clear, relevant for the field and presented in a well-structured manner? Please kindly note that if reviewers are asked to assess a manuscript they previously reviewed for another journal, this is not considered to be a conflict of interest. 3, pp. Support vector machine (SVM) and extreme gradient boosting (XGB) machine learning models were used to predict groundwater aflaj potential in the Nizwa watershed in the Sultanate of Oman (Oman). The High Availability and Visibility of our open access articles is guaranteed through the free and unlimited accessibility of the publication over the Internet. In addition, there are still many problems to be discovered and tested using a wide variety of settings in machine learning for the mental health domain. paper provides an outlook on future directions of research or possible applications. Machine learning has offered essential advantages to a wide range of areas such as speech recognition, computer vision, and natural language processing. 7, no. For instance, Psycho Web is being developed where the application allows users to collect and predict the data from mental health patients using machine learning [58]. Papers are submitted upon individual invitation or recommendation by the scientific editors and undergo peer review Machine learning is a technique that aims to construct systems that can improve through experience by using advanced statistical and probabilistic techniques. https://doi.org/10.3390/molecules25245789, Breijyeh, Zeinab, and Rafik Karaman. Then, ten classifiers as shown in Table 1 were selected to predict the anxiety and depression in elderly patients. Then, the support vector machine algorithm is used for classification with the help of functional magnetic resonance imaging and single nucleotide polymorphism. First of all, we want to provide a summary of the latest research on machine learning approaches in predicting mental health problems, which can give useful information to the clinical practice. Some of the research papers highlighted such amount of the data set, which are Greenstein et al. Whether the data was adequate to test the proposed hypotheses by satisfying the approved outcome-neutral conditions (such as quality checks, positive controls); Whether the stated hypotheses tested was the same as the approved Stage 1 submission; Whether the authors adhered precisely to the registered experimental procedures or were able to sufficiently justify any changes; Whether any new analyses (not mentioned at Stage 1) are methodologically sound and relevant; Whether the authors conclusions are justified given the data. Reviewing for MDPI journals brings the following benefits: The Reviewer Board (RB) consists of experienced researchers whose main responsibility is to regularly and actively support journals by providing high quality, rigorous, and transparent review reports for submitted manuscripts within their area of expertise. 1Atrey P K, Hossain M A, El Saddik A, et al. As such, this systematic literature review paper aims both to cover recent advancements in this field in addition to providing a focused critical summary concerning the gaps in the literature in terms of the applications of machine learning in the mental health field and to subsequently highlight potential avenues for future research. Meanwhile, the input involves the inclusion of cortisol and grey matter volume can reach accuracies of 90.10% and 67.46% for the classification of case and disorder, respectively. [41], and Marmar et al. Accepted manuscripts are then copy-edited and English-edited internally. Find support for a specific problem in the support section of our website. H. Yang, J. Liu, J. Sui, G. Pearlson, and V. D. Calhoun, A hybrid machine learning method for fusing fMRI and genetic data: combining both improves classification of schizophrenia, Frontiers in Human Neuroscience, vol. 3, pp. The aim is to provide a snapshot of some of the most exciting work [38], Reece et al. 7, pp. Visit our dedicated information section to learn more about MDPI. Are the conclusions consistent with the evidence and arguments presented? 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. This paper presents a recent systematic review of machine learning approaches in predicting mental health problems. [34] and Grotegerd et al. In order to be human-readable, please install an RSS reader. [48] have shown that the support vector machine can achieve an accuracy of 83.59% when classifying the PTSD among male soldiers. Then, the logistic regression scored an AUC of 0.700, and the XGBoost performed on the test set with an AUC of 0.692. In a research article by Roberts et al., a support vector machine is used to distinguish bipolar disorder patients, risk subjects, and healthy controls [38]. From time to time, machine learning and data mining approaches continue to develop rapidly. 29, no. It publishes original research articles, reviews, tutorials, research ideas, short notes and Special Issues that focus on machine learning and applications. You seem to have javascript disabled. Besides that, this paper is targeting the practitioners in the machine learning communities where they can keep updated on the application of machine learning nowadays particularly in the mental health field. [35], Valenza et al. Ustawienia polityki cookies mona zmieni w opcjach przegldarki.W przegldarce internetowej mona zmieni ustawienia dotyczce cookies. interesting to authors, or important in this field. It is capable of adopting self-defined pseudolabels as supervision and use the learned representations for several downstream tasks. 1, p. 2776, 2009. Performance of machine learning models and being explainable are necessary for mental health problems. The role of the reviewer is vital and bears a great responsibility in ensuring the integrity of the scholarly record. //-->. Individuals that have a social anxiety disorder are frequently afraid of social situations. Our internal staff will check your research background and any potential conflicts of interest. Gdzie cisza i spokj pozwoli na relaks, a ziele nacieszy wzrok. D. Vergyri, B. Knoth, E. Shriberg et al., Speech-based assessment of PTSD in a military population using diverse feature classes, in Proceedings of the INTERSPEECH 2015, Dresden, Germany, 2015. You can provide references as needed, but they must clearly improve the quality of the manuscript under review. //--> 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. The Feature Paper can be either an original research article, a substantial novel research study that often involves After that, machine learning is being applied to label schizophrenia patients and health controls. Google Scholar; Ying-Hsiu Lai and Shang-Hong Lai. Dla Pastwa wygody Serwis www.inwestor.glogow.pl uywa plikw cookies m.in. The authors utilized the clinical data, psychological questionnaires, and localization variables when conducting the research. Editors select a small number of articles recently published in the journal that they believe will be particularly The statistics provided in Figure 5 shows the trends of the reviewed research articles and papers based on the years. The corresponding author was supported by a research grant from the Ministry of Higher Education, Malaysia (Fundamental Research Grant Scheme (FRGS), Dana Penyelidikan, Kementerian Pengajian Tinggi, FRGS/1/2019/ICT02/UMS/01/1). Individuals that experience mania often exhibit reckless behaviours. those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). Moreover, some studies are conducted by using a partial large size of the data set, which is above 100 subjects. Most of the research articles show that machine learning models have obtained the accuracy of above 70%. A. Vabalas, E. Gowen, E. Poliakoff, and A. J. Casson, Machine learning algorithm validation with a limited sample size, PLoS One, vol. For the single nucleotide polymorphism, an accuracy of 74% is obtained. Random forest has achieved a satisfying accuracy score of 81.2%; meanwhile, logistic regression obtained an accuracy score of 77.8%. Please let us know what you think of our products and services. [50] have utilized large size of data set in the prediction of the mental health problems, which are above 300 subjects. Hence, the convolutional neural network can be a helpful model to assist psychologists and counsellors for making the treatments efficient. [23], but Jo et al. Other research articles show different results obtained from bipolar disorder prediction with machine learning models. Besides that, psychological tests and assessment tools are also available and are used to diagnose a person for mental health problems. Homogeneous fusion. Automotive manufacturing workers in Chongqing city surveyed during 2019–2021 were used as the study subjects. 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. Is the review clear, comprehensive and of relevance to the field? Ustawienia polityki cookies mona zmieni w opcjach przegldarki. Shokrollahi, Y.; Dong, P.; Gamage, P.T. Breijyeh, Z.; Karaman, R. Comprehensive Review on Alzheimers Disease: Causes and Treatment. In a research article by Leightley et al. Please maintain a neutral tone and focus on providing constructive criticism that will help the authors improve their work. J. Angst and R. Sellaro, Historical perspectives and natural history of bipolar disorder, Biological Psychiatry, vol. Besides that, the performance of the machine learning mechanisms that are being applied has been highlighted because it could provide benefits within the medical field in data mining or big data fields. In short, data mining is a crucial technique in the role of computer science. Supervised learning is excellent at classification and regression problems. They are utilized as tools in assisting for medical diagnosis as they became more reliable in their performance. permission provided that the original article is clearly cited. [45]. Molecules. However, this machine learning model can reach an accuracy score of 96.36% when predicting bipolar disorders as stated by Akinci et al. 26602673, 2017. [24], and Pinaya et al. [36], and Sumathi and Poorna [49] have conducted the classification experiments with different types of data set. Please note that many of the page functionalities won't work as expected without javascript enabled. 28432864, 2017. [39]. The authors declare that they have no conflicts of interest. The existing studies and research show that machine learning can be a useful tool in helping understand psychiatric disorders. Please evaluate the ethics statements and data availability statements to ensure they are adequate. From the results, Marmar et al. The analysis phase is started by finding out and investigating the performance of the machine learning approaches that were used to diagnose or predict 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/. 17351780, 1997. [21] show that random forest obtains a low accuracy, which is 68.9%. Disclaimer/Publishers Note: The statements, opinions and data contained in all publications are solely 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. 4, pp. The limitations, drawbacks, or gaps of the research will be discussed and examined in this part. 4, pp. Bipolar disorder is another mental disorder identified by the episode of mania and depression. A systematic review of deep learning-based multimodal remote sensing data fusion. R. N. Plschke, E. C. Cieslik, V. I. Mller et al., On the integrity of functional brain networks in schizophrenia, Parkinsons disease, and advanced age: evidence from connectivity-based single-subject classification, Human Brain Mapping, vol. The research involves the data from resting functional connectivity of the left inferior frontal gyrus. In one of the research works conducted by Jo et al., they used network analysis and machine learning approaches to identify 48 schizophrenia patients and 24 healthy controls [21]. The accuracy of each machine learning algorithm is obtained and recorded. The humpback has a distinctive body shape, with long pectoral fins and a knobbly head. 11, pp. Based on the experimental observation of BG-COL composite hydrogels with scanning electron microscope, 2000 microstructural images with randomly distributed BG particles were created. 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