scholarly journals Predicting Mental Health Illness using Machine Learning Algorithms

2022 ◽  
Vol 2161 (1) ◽  
pp. 012021
Author(s):  
Konda Vaishnavi ◽  
U Nikhitha Kamath ◽  
B Ashwath Rao ◽  
N V Subba Reddy
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Well Being ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Childhood And Adolescence ◽  
Health Issues ◽  
Mental Health Issues ◽  
Learning Techniques ◽  
Tree Classifier

Abstract Early detection of mental health issues allows specialists to treat them more effectively and it improves patient’s quality of life. Mental health is about one’s psychological, emotional, and social well-being. It affects the way how one thinks, feels, and acts. Mental health is very important at every stage of life, from childhood and adolescence through adulthood. This study identified five machine learning techniques and assessed their accuracy in identifying mental health issues using several accuracy criteria. The five machine learning techniques are Logistic Regression, K-NN Classifier, Decision Tree Classifier, Random Forest, and Stacking. We have compared these techniques and implemented them and also obtained the most accurate one in Stacking technique based with an accuracy of prediction 81.75%.

scholarly journals An Integrated Approach to Deal with Mental Health Issues of Children and Adolescent during COVID-19 Pandemic

2020 ◽  
Author(s):  
Mousumi Sethy ◽  
Reshmi Mishra
Keyword(s):  
Mental Health ◽  
Children And Adolescents ◽  
Adult Life ◽  
Holistic Approach ◽  
Integrated Approach ◽  
Well Being ◽  
Childhood And Adolescence ◽  
Health Issues ◽  
Children And Adolescent ◽  
Mental Health Issues

The pandemic caused by COVID-19 has left few countries untouched. It is a far-reaching implication on humankind, with children and adolescents, being no exception. Although the prevalence and fatality are negligible among children, a possible impact on their psychological and mental health cannot be disregarded. The unprecedented change in the way of living is bound to be having some psychological consequences on children and adolescents. The experiences gathered in childhood and adolescence are known to contribute to shaping the physical, emotional, and social well-being in adult life. Children are highly susceptible to environmental stressors. The present situation has the potential of adversely affecting the physical and mental well-being of children. To save the children from the long term consequences of this pandemic, a holistic approach integrating biological, psychological, social and spiritual methods of enhancing mental health have become essential. A concerted effort of government, Non Government Organisations (NGOs), parents, teachers, schools, psychologists, counselors and physicians are required to deal with the mental health issues of children and adolescents. This paper discusses the possible role of these agencies in the holistic intervention of this crisis.

scholarly journals Review on Machine Learning Techniques to predict Bipolar Disorder

2021 ◽  
Author(s):  
Nisha Agnihotri
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Bipolar Disorder ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Brain Disorder ◽  
Research Papers ◽  
Learning Techniques

<i>Bipolar disorder, a complex disorder in brain has affected many millions of people around the world. This brain disorder is identified by the occurrence of the oscillations of the patient’s changing mood. The mood swing between two states i.e. depression and mania. This is a result of different psychological and physical features. A set of psycholinguistic features like behavioral changes, mood swings and mental illness are observed to provide feedback on health and wellness. The study is an objective measure of identifying the stress level of human brain that could improve the harmful effects associated with it considerably. In the paper, we present the study prediction of symptoms and behavior of a commonly known mental health illness, bipolar disorder using Machine Learning Techniques. Therefore, we extracted data from articles and research papers were studied and analyzed by using statistical analysis tools and machine learning (ML) techniques. Data is visualized to extract and communicate meaningful information from complex datasets on predicting and optimizing various day to day analyses. The study also includes the various research papers having machine Learning algorithms and different classifiers like Decision Trees, Random Forest, Support Vector Machine, Naïve Bayes, Logistic Regression and K- Nearest Neighbor are studied and analyzed for identifying the mental state in a target group. The purpose of the paper is mainly to explore the challenges, adequacy and limitations in detecting the mental health condition using Machine Learning Techniques</i>

scholarly journals Review on Machine Learning Techniques to predict Bipolar Disorder

2021 ◽  
Author(s):  
Nisha Agnihotri
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Bipolar Disorder ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Brain Disorder ◽  
Research Papers ◽  
Learning Techniques

<i>Bipolar disorder, a complex disorder in brain has affected many millions of people around the world. This brain disorder is identified by the occurrence of the oscillations of the patient’s changing mood. The mood swing between two states i.e. depression and mania. This is a result of different psychological and physical features. A set of psycholinguistic features like behavioral changes, mood swings and mental illness are observed to provide feedback on health and wellness. The study is an objective measure of identifying the stress level of human brain that could improve the harmful effects associated with it considerably. In the paper, we present the study prediction of symptoms and behavior of a commonly known mental health illness, bipolar disorder using Machine Learning Techniques. Therefore, we extracted data from articles and research papers were studied and analyzed by using statistical analysis tools and machine learning (ML) techniques. Data is visualized to extract and communicate meaningful information from complex datasets on predicting and optimizing various day to day analyses. The study also includes the various research papers having machine Learning algorithms and different classifiers like Decision Trees, Random Forest, Support Vector Machine, Naïve Bayes, Logistic Regression and K- Nearest Neighbor are studied and analyzed for identifying the mental state in a target group. The purpose of the paper is mainly to explore the challenges, adequacy and limitations in detecting the mental health condition using Machine Learning Techniques</i>

Prediction Models for Healthcare Using Machine Learning

2021 ◽  
pp. 70-91
Author(s):  
Ayushe Gangal ◽  
Peeyush Kumar ◽  
Sunita Kumari ◽  
Anu Saini
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Heart Diseases ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Brain Diseases ◽  
Health Issues ◽  
Industry Study ◽  
Learning Techniques

Healthcare is always a sensitive issue for all of us, and it will always remain. Predicting various types of health issues in advance can lead us to a better life. Various types of health problems are there like cancer, heart diseases, diabetes, arthritis, pneumonia, lungs disease, liver disease, and brain disease, which all are at high risk. To reduce the risk of health issues, some suitable models are needed for prediction. Thus, it became as a motivational factor for the authors to survey the existing literature on this topic thoroughly and have consequently to identify suitable machine learning techniques so that improvement can be possible while selecting a prediction model. In this chapter, concept of survey is used to provide the prediction models for healthcare issues along with the challenges associated with each model. This chapter will broadly cover the following: machine learning algorithms used in health industry, study various prediction models for Cancer, Heart diseases, Diabetes and Brain diseases, comparative study of various machine learning algorithms used for prediction.

scholarly journals Breast Cancer Prediction Using Classification Techniques of Machine Learning

2022 ◽  
Vol 10 (1) ◽  
pp. 51-57
Author(s):  
Angela More
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Decision Tree Classifier ◽  
Learning Techniques ◽  
Tree Classifier ◽  
Abstract Data

Abstract: Data analytics play vital roles in diagnosis and treatment in the health care sector. To enable practitioner decisionmaking, huge volumes of data should be processed with machine learning techniques to produce tools for prediction and classification Breast Cancer reports 1 million cases per year. We have proposed a prediction model, which is specifically designed for prediction of Breast Cancer using Machine learning algorithms Decision tree classifier, Naïve Bayes, SVM and KNearest Neighbour algorithms. The model predicts the type of tumour, the tumour can be benign (noncancerous) or malignant (cancerous) . The model uses supervised learning which is a machine learning concept where we provide dependent and independent columns to machine. It uses classification technique which predicts the type of tumour. Keywords: Cancer, Machine learning, Prediction, Data Visualization, SVM, Naïve Bayes, Classification.

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

scholarly journals Participation in community gardening: Sowing the seeds of well-being

2021 ◽  
pp. 000841742199438
Author(s):  
Melinda J. Suto ◽  
Shelagh Smith ◽  
Natasha Damiano ◽  
Shurli Channe
Keyword(s):  
Mental Health ◽  
Social Connectedness ◽  
Community Gardens ◽  
Well Being ◽  
Supported Housing ◽  
Research Approach ◽  
Subjective Well Being ◽  
Health Issues ◽  
Community Gardening ◽  
Mental Health Issues

Background. Sustaining well-being challenges people with serious mental health issues. Community gardening is an occupation used to promote clients’ well-being, yet there is limited evidence to support this intervention. Purpose. This paper examines how facilitated community gardening programs changed the subjective well-being and social connectedness of people living with mental health issues. Method. A community-based participatory research approach and qualitative methods were used with 23 adults living in supported housing and participating in supported community gardening programs. A constructivist approach guided inductive data analysis. Findings. Participation in community gardening programs enhanced well-being through welcoming places, a sense of belonging, and developing positive feelings through doing. The connection to living things and responsibility for plants grounded participants in the present and offered a unique venue for learning about gardening and themselves. Implications. Practitioners and service-users should collaborate to develop leadership, programs, places, and processes within community gardens to enhance well-being.

scholarly journals Feasibility of Machine Learning Algorithms for Predicting the Deformation of Anodic Titanium Films by Modulating Anodization Processes

Materials ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1089
Author(s):  
Sung-Hee Kim ◽  
Chanyoung Jeong
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Multiclass Classification ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Smart Manufacturing ◽  
Gradient Boosting ◽  
Experimental Conditions ◽  
Learning Techniques ◽  
Tio2 Nanostructures

This study aims to demonstrate the feasibility of applying eight machine learning algorithms to predict the classification of the surface characteristics of titanium oxide (TiO2) nanostructures with different anodization processes. We produced a total of 100 samples, and we assessed changes in TiO2 nanostructures’ thicknesses by performing anodization. We successfully grew TiO2 films with different thicknesses by one-step anodization in ethylene glycol containing NH4F and H2O at applied voltage differences ranging from 10 V to 100 V at various anodization durations. We found that the thicknesses of TiO2 nanostructures are dependent on anodization voltages under time differences. Therefore, we tested the feasibility of applying machine learning algorithms to predict the deformation of TiO2. As the characteristics of TiO2 changed based on the different experimental conditions, we classified its surface pore structure into two categories and four groups. For the classification based on granularity, we assessed layer creation, roughness, pore creation, and pore height. We applied eight machine learning techniques to predict classification for binary and multiclass classification. For binary classification, random forest and gradient boosting algorithm had relatively high performance. However, all eight algorithms had scores higher than 0.93, which signifies high prediction on estimating the presence of pore. In contrast, decision tree and three ensemble methods had a relatively higher performance for multiclass classification, with an accuracy rate greater than 0.79. The weakest algorithm used was k-nearest neighbors for both binary and multiclass classifications. We believe that these results show that we can apply machine learning techniques to predict surface quality improvement, leading to smart manufacturing technology to better control color appearance, super-hydrophobicity, super-hydrophilicity or batter efficiency.

scholarly journals Mental Health in Women Living With HIV: The Unique and Unmet Needs

2021 ◽  
Vol 20 ◽  
pp. 232595822098566
Author(s):  
Elizabeth M. Waldron ◽  
Inger Burnett-Zeigler ◽  
Victoria Wee ◽  
Yiukee Warren Ng ◽  
Linda J. Koenig ◽  
...  
Keyword(s):  
Mental Health ◽  
Mental Health Treatment ◽  
Behavioral Therapy ◽  
Well Being ◽  
Future Research ◽  
Health Issues ◽  
Women Living With Hiv ◽  
Mental Health Issues ◽  
Hiv Management ◽  
Living With Hiv

Women living with HIV (WLWH) experience depression, anxiety, and posttraumatic stress symptoms at higher rates than their male counterparts and more often than HIV-unaffected women. These mental health issues affect not only the well-being and quality of life of WLWH, but have implications for HIV management and transmission prevention. Despite these ramifications, WLWH are under-treated for mental health concerns and they are underrepresented in the mental health treatment literature. In this review, we illustrate the unique mental health issues faced by WLWH such as a high prevalence of physical and sexual abuse histories, caregiving stress, and elevated internalized stigma as well as myriad barriers to care. We examine the feasibility and outcomes of mental health interventions that have been tested in WLWH including cognitive behavioral therapy, mindfulness-based interventions, and supportive counseling. Future research is required to address individual and systemic barriers to mental health care for WLWH.

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