A comprehensive review of predictive analytics models for mental illness using machine learning algorithms (original) (raw)
Related papers
Using Machine Learning to Predict Mental Illness
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2023
Depression and anxiety are omnipresent but largely unnoticeable to us. The statistics indicate that most families on the planet are undoubtedly impacted by them, although many people suffer in silence. Now, this is somewhat due to the difficulty in discussing these diseases, but it's also due to the difficulty in obtaining care. In order to forecast mental health issues, this survey presents a current thorough assessment of machine learning algorithms. We will also go over the difficulties, restrictions, and future directions for using machine learning in the field of mental health.
Predicting Mental Illness using Social Media Posts and Comments
2020
From the last decade, a significant increase of social media implications could be observed in the context of e-health. The medical experts are using the patient's post and their feedbacks on social media platforms to diagnose their infectious diseases. However, there are only few studies who have leveraged the capabilities of machine learning (ML) algorithms to classify the patient's mental disorders such as Schizophrenia, Autism, and Obsessive-compulsive disorder (OCD) and Post-traumatic stress disorder (PTSD). Moreover, these studies are limited to large number of posts and relevant comments which could be considered as a threat for their effectiveness of their proposed methods. In contrast, this issue is addressed by proposing a novel ML methodology to classify the patient's mental illness on the basis of their posts (along with their relevant comments) shared on the well-known social media platform "Reddit". The proposed methodology is exploit by leveragin...
Mental Health Prediction Using Machine Learning
IRJET, 2022
Mental health problems are one of the major concerns of the 21st century in the field of healthcare. One of the major reasons behind this problem is lack of awareness among masses. Our aim with this paper is to help people realize that they might be suffering from some kind of mental problem like depression, anxiety, ptsd, insomnia by making them aware of their symptoms using Machine learning. In order to apply the machine learning algorithms, data was collected from individuals of varied ages, professions, sex and lifestyle through survey form consisting of questions, which are often used by psychologists to understand their patient's problem in detail. We believe implementation of such a system could help us prevent potential "Mental health epidemic" and give people easy access to diagnosis.
Predicting Mental Health Outcomes Using Wearable Device Data and Machine Learning
International Journal of Innovative Science and Research Technology (IJISRT), 2021
This paper proposes a machine learningbased system designed to predict mental health outcomes using wearable device data. The system is conceptualized to process physiological and behavioral data such as heart rate, sleep patterns, and activity levels collected from wearable technology. Key stages of the system include data preprocessing, feature extraction, and model training using multiple machine-learning algorithms, including Random Forest, Support Vector Machine, XGBoost, and Logistic Regression. These models are combined using a voting-based ensemble classifier to improve prediction accuracy. While the system has not yet been implemented, expected results suggest that this approach will enhance prediction reliability and offer real-time insights into mental health conditions. The proposed system is envisioned to facilitate early detection of mental health disorders, thereby aiding in timely interventions and personalized care.
International Journal of Experimental Research and Review, 2024
Mental health disorders, including anxiety, depres-sion, and stress, profoundly impact individuals’ well-being and necessitate effective early detection for timely intervention. This research investigates the predictive capabilities of machine learning algorithms in assessing anxiety, depression, and stress levels based on questionnaire-derived scores. Utilizing a dataset comprising self-reported scores obtained through a tailored questionnaire designed for mental health assessment, we delve into the application of Decision Trees, Naive Bayes, Support Vector Machines (SVM), and Random Forests for prediction. Data preprocessing involved comprehensive cleaning, encoding categorical variables, and careful feature selection, ensuring the relevance of features in the predictive models. Each algorithm un-derwent individual implementation, wherein we scrutinized their performances in predicting mental health conditions. Evaluation metrics such as accuracy, precision, and recall were employed to assess the models’ proficiency in predicting anxiety, depression, and stress levels. The findings underscore the potential of machine learning in accurately predicting mental health conditions based on questionnaire responses, offering insights into personalized interventions and early detection systems. This study contributes to advancing the understanding of machine learning applications in mental health assessment, highlighting avenues for impactful interventions in mental health care.
Quantifying the efficacy of ML models at predicting mental health illnesses
IRJET, 2022
Machine Learning has become increasingly pervasive in the field of medicine. Though the large majority of ML-based research focuses on detecting tumors, brain damage, and physical injuries, mental health has not received much attention. The current machine learning models typically fail to consider emotional variability and the extremes of data points when predicting the prevalence of depression. Furthermore, these models don’t align with universally-accepted models like Beck’s Depression Inventory. It is hypothesized that emotional variability, level of depressive symptoms, amount of labeled data and features correlate with improvements in the accuracy of an ML model. The preliminary results suggest that there is a positive correlation between the level of emotional variability and the amount of labeled data and features with a model’s accuracy. In this study, we considered the ability to predict depression through self-reporting, where emotional variability was taken into account through a novel baseline model (which uses a participant’s most frequently responded answer). Discussing the findings, we considered (i) an effective means for data collection through questionnaires was developed, (ii) a necessary quantitative improvement for each model was constructed, and (iii ) a random forest classifier was found to be the best ML model to predict the incidence of depression. In brief, this research paper assesses the accuracy, reliability, and effectiveness of these ML algorithms, as well as the benefits and drawbacks of the implementation of these algorithms. Though further work and a larger-scale study are required, this paper takes a step in the right direction in self-reporting depression
Mental Health Analysis in Social Media Posts: A Survey
Archives of Computational Methods in Engineering
The surge in internet use to express personal thoughts and beliefs makes it increasingly feasible for the social NLP research community to find and validate associations between social media posts and mental health status. Cross-sectional and longitudinal studies of social media data bring to fore the importance of real-time responsible AI models for mental health analysis. Aiming to classify the research directions for social computing and tracking advances in the development of machine learning (ML) and deep learning (DL) based models, we propose a comprehensive survey on quantifying mental health on social media. We compose a taxonomy for mental healthcare and highlight recent attempts in examining social well-being with personal writings on social media. We define all the possible research directions for mental healthcare and investigate a thread of handling online social media data for stress, depression and suicide detection for this work. The key features of this manuscript are (i) feature extraction and classification, (ii) recent advancements in AI models, (iii) publicly available dataset, (iv) new frontiers and future research directions. We compile this information to introduce young research and academic practitioners with the field of computational intelligence for mental health analysis on social media. In this manuscript, we carry out a quantitative synthesis and a qualitative review with the corpus of over 92 potential research articles. In this context, we release the collection of existing work on suicide detection in an easily accessible and updatable repository:https:// github. com/ drmus kanga rg/ menta lheal thcare.
Machine Learning and Knowledge Discovery in Databases, 2019
Predicting mental health from smartphone and social media data on a longitudinal basis has recently attracted great interest, with very promising results being reported across many studies [3,9,13,26]. Such approaches have the potential to revolutionise mental health assessment, if their development and evaluation follows a real world deployment setting. In this work we take a closer look at state-of-the-art approaches, using different mental health datasets and indicators, different feature sources and multiple simulations, in order to assess their ability to generalise. We demonstrate that under a pragmatic evaluation framework, none of the approaches deliver or even approach the reported performances. In fact, we show that current state-of-the-art approaches can barely outperform the most naïve baselines in the real-world setting, posing serious questions not only about their deployment ability, but also about the contribution of the derived features for the mental health assessment task and how to make better use of such data in the future.
Application of Machine Learning Methods in Mental Health Detection: A Systematic Review
IEEE Access, 2020
This paper presents a critical assessment analysis on mental health detection in Online Social Networks (OSNs) based on the data sources, machine learning techniques, and feature extraction method. The appropriateness of the mental health detection was also investigated by identifying its data analysis method, comparison, challenges, and limitations. This study reviewed articles published in major databases between 2007 and 2018 through keyword searches. The articles were screened base on their titles and abstracts before the full texts were reviewed. The articles were coded in accordance with data set (e.g., data sources, keywords, and geographical locations), method of data analysis, machine learning or deep learning technique, classifier performance, and feature extraction method. 22 articles were selected for review from the total of 2770. As OSNs exhibit high potential as a data source in early detection of mental health problems, most researchers used text analysis on a new data set extracted from different OSNs sources. The extracted data were examined using a statistical analysis or machine learning techniques. Several studies also applied multimethod techniques, which included distributing questionnaires while requesting for the respondents' consent to later access and extract information from his/her OSNs account. Big data in OSNs contribute on mental health problem detection. The presented method is an alternative approach to the early detection of mental health problems rather than using traditional strategies, such as collecting data through questionnaires or devices and sensors, which are time-consuming and costly. However, mental health problem detection through OSNs necessitates a comprehensive adoption, innovative algorithms, and computational linguistics to describe its limitations and challenges. Moreover, referrals from mental health specialists as subject matter experts are also required to help obtain accurate and effective information. INDEX TERMS Deep learning, feature extraction, machine learning, mental health, online social network.
Data-driven approaches to tackling mental health
World Journal of Advanced Research and Review, 2024
Background: Over the past few years there has been immense evolution in various areas particularly in the areas of digital technologies wherein the pace of change is very high. Industrial areas such as operations and supply chain management together with advanced technologies such as machine learning, big data analytics, artificial intelligence, as well as the Internet of Things, create completely different forms of operational models for various industries. In the area of healthcare too, these emerging computational sophistication is introduced to revolutionise the approaches to prevent, diagnose and treat diverse diseases and illnesses. Objective: The objective of this study is to provide an extensive review of the contemporary approaches utilizing data to cope with significant mental disorders. From over 60 relevant scholarly articles published between 2011 and 2023, it discusses how tools such as predictive modelling, social media analysis, data from smartphones, and chatbots help with issues such as early detection, telemonitoring, provision of psychological support, and individualised prevention. Method: An initial literature review to analyse over 60 research articles, which include empirical studies that were conducted between 2011 and 2023. The research assessed implemented novel digital approaches to mental health interventions including big data analytics for predicting condition status, machine learning for examining social media content, behaviour monitoring through smartphone sensors, and using conversational agents or chatbots. The following is an overview of general conclusions from experimental and descriptive secondary research studies published in professional outlets concerning possible advantages and disadvantages of data science applied to important concerns in mental health. Results: Research reveals that integrating subtle e-health tools in tandem with typical treatment approaches holds the potential to expand mental health services to more or less integrate them into clients' day-today lives, and practically individualize effective treatments accordingly. Technological solutions for instance allow remote risk assessment, symptom monitoring and determination of treatment compliance. New lines of virtualized paradigm solve social challenges that interfere with the conventional provision and consumption of care. However, questions of privacy and the long-term effects as well as clinical adoption are yet to be solved in a analytically distinct manner. Discussion: Despite there is a great number of opportunities, certain critical issues need to be solved to unleash the full potential of the data-driven approach in mental health care. Namely, technology integration into the streams of a