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Data Analysis and Machine Learning in Epidemiology of Mental and Behavioral Disorders

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: 20 March 2025 | Viewed by 996

Special Issue Editor


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Guest Editor
1. Medical Analysis Expert Group, Institute of Technology, Universidad de Castilla-La Mancha, 16071 Cuenca, Spain
2. Medical Analysis Expert Group, Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), 45071 Toledo, Spain
Interests: machine learning; biomedical signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the realm of healthcare and epidemiology, the convergence of data analysis, machine learning, and the study of mental and behavioral disorders is of immense significance. The evolving landscape of these fields is marked by new challenges and unprecedented opportunities, and requires a platform to showcase innovative ideas and experimental findings. We are pleased to announce this Special Issue, which is dedicated to the fusion of data-driven approaches, machine learning techniques, and epidemiological research in the context of mental and behavioral disorders. This issue aims to bridge the gap between research theories, practical applications, and service delivery, with the aim of advancing our understanding and management of these critical health concerns.

This Special Issue, entitled “Data Analysis and Machine Learning in Epidemiology of Mental and Behavioral Disorders”, encompasses a broad range of topics, reflecting the interdisciplinary nature of contemporary research and development. These areas of interest include, but are not limited to:

Epidemiological Data Analysis: Given the increasing availability of healthcare data, novel approaches to the analysis of epidemiological data are essential for uncovering patterns, risk factors, and trends related to mental and behavioral disorders.

Machine Learning and Predictive Modeling: Machine learning techniques have the potential to revolutionize the prediction and early detection of mental and behavioral disorders. We welcome contributions that explore the development of predictive models and their clinical applications.

Large-Scale Data Sets: The collection and analysis of extensive datasets, ranging from patient records to social media data, have the potential to provide a deeper understanding of these disorders. The submission of research focusing on the processing and utilization of voluminous data is highly encouraged.

Diagnosis and Treatment Optimization: Machine learning can aid in the refinement of diagnostic criteria and the personalization of treatment approaches. Original research in this area is of great interest.

Public Health Interventions: The application of data-driven insights to public health interventions, including targeted outreach and prevention programs, is crucial in addressing mental and behavioral disorders at a population level.

In this Special Issue, we invite authors to submit high-quality, original research papers that delve into the intricacies and challenges of the aforementioned topics. Our aim is to foster interdisciplinary dialogues and facilitate the exchange of knowledge among experts in the following overlapping fields:

  • Epidemiology;
  • Data analysis in healthcare;
  • Machine learning and artificial intelligence in healthcare;
  • Public health.

We believe that these contributions will be instrumental in advancing our understanding of mental and behavioral disorders, ultimately contributing to improved healthcare outcomes and the well-being of individuals and communities.

Dr. Ana María Torres Aranda
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • epidemiology
  • mental health
  • behavioral disorders
  • data analysis
  • machine learning
  • healthcare data
  • predictive modeling
  • public health interventions

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Published Papers (1 paper)

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Research

20 pages, 2670 KiB  
Article
Enhancing Depression Detection: A Stacked Ensemble Model with Feature Selection and RF Feature Importance Analysis Using NHANES Data
by Annapoorani Selvaraj and Lakshmi Mohandoss
Appl. Sci. 2024, 14(16), 7366; https://doi.org/10.3390/app14167366 - 21 Aug 2024
Viewed by 696
Abstract
Around the world, 5% of adults suffer from depression, which is often inadequately treated. Depression is caused by a complex relationship of cultural, psychological, and physical factors. This growing issue has become a significant public health problem globally. Medical datasets often contain redundant [...] Read more.
Around the world, 5% of adults suffer from depression, which is often inadequately treated. Depression is caused by a complex relationship of cultural, psychological, and physical factors. This growing issue has become a significant public health problem globally. Medical datasets often contain redundant characteristics, missing information, and high dimensionality. By using an iterative floating elimination feature selection algorithm and considering various factors, we can reduce the feature set and achieve optimized outcomes. The research utilizes the 36-Item Short Form Survey (SF-36) from the NHANES 2015–16 dataset, which categorizes data into seven groups relevant to quality of life and depression. This dataset presents a challenge due to its imbalance, with only 8.08% of individuals diagnosed with depression. The Depression Ensemble Stacking Generalization Model (DESGM) employs stratified k-fold cross-validation and oversampling for training data. DESGM enhances the classification performance of both base learners (linear support vector machine, perceptron, artificial neural network, linear discriminant analysis, and K-nearest neighbor) and meta-learners (logistic regression). The model achieved an F1 score of 0.9904 and an accuracy of 98.17%, with no instances of depression misdiagnosed. Full article
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