Depression detection from sMRI and rs-fMRI images using machine learning

2021 ◽  
Author(s):  
Marzieh Mousavian ◽  
Jianhua Chen ◽  
Zachary Traylor ◽  
Steven Greening
Keyword(s):  
Machine Learning ◽  
Depression Detection

Depression Detection using Machine Learning

2021 ◽  
Author(s):  
Kumbhar P.Y. ◽  
Rajendra Dube ◽  
Sudhakar Barbade ◽  
Gayatri Kulkarni ◽  
Nikita Konda ◽  
...  
Keyword(s):  
Machine Learning ◽  
Depression Detection

scholarly journals An EEG-Based Depression Detection Method Using Machine Learning Model

2021 ◽  
Vol 10 (1) ◽  
pp. 17-22
Author(s):  
Ran Bai ◽  
◽  
Yu Guo ◽  
Xianwu Tan ◽  
Lei Feng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Detection Method ◽  
Learning Model ◽  
Machine Learning Model ◽  
Depression Detection

scholarly journals Detection of child depression using machine learning methods

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0261131
Author(s):  
Umme Marzia Haque ◽  
Enamul Kabir ◽  
Rasheda Khanam
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Mental Illness ◽  
Children And Adolescents ◽  
Mental Health Problems ◽  
Confusion Matrix ◽  
Model Performance ◽  
Child And Adolescent ◽  
Target Variable ◽  
Depression Detection

Background Mental health problems, such as depression in children have far-reaching negative effects on child, family and society as whole. It is necessary to identify the reasons that contribute to this mental illness. Detecting the appropriate signs to anticipate mental illness as depression in children and adolescents is vital in making an early and accurate diagnosis to avoid severe consequences in the future. There has been no research employing machine learning (ML) approaches for depression detection among children and adolescents aged 4–17 years in a precisely constructed high prediction dataset, such as Young Minds Matter (YMM). As a result, our objective is to 1) create a model that can predict depression in children and adolescents aged 4–17 years old, 2) evaluate the results of ML algorithms to determine which one outperforms the others and 3) associate with the related issues of family activities and socioeconomic difficulties that contribute to depression. Methods The YMM, the second Australian Child and Adolescent Survey of Mental Health and Wellbeing 2013–14 has been used as data source in this research. The variables of yes/no value of low correlation with the target variable (depression status) have been eliminated. The Boruta algorithm has been utilized in association with a Random Forest (RF) classifier to extract the most important features for depression detection among the high correlated variables with target variable. The Tree-based Pipeline Optimization Tool (TPOTclassifier) has been used to choose suitable supervised learning models. In the depression detection step, RF, XGBoost (XGB), Decision Tree (DT), and Gaussian Naive Bayes (GaussianNB) have been used. Results Unhappy, nothing fun, irritable mood, diminished interest, weight loss/gain, insomnia or hypersomnia, psychomotor agitation or retardation, fatigue, thinking or concentration problems or indecisiveness, suicide attempt or plan, presence of any of these five symptoms have been identified as 11 important features to detect depression among children and adolescents. Although model performance varied somewhat, RF outperformed all other algorithms in predicting depressed classes by 99% with 95% accuracy rate and 99% precision rate in 315 milliseconds (ms). Conclusion This RF-based prediction model is more accurate and informative in predicting child and adolescent depression that outperforms in all four confusion matrix performance measures as well as execution duration.

scholarly journals Application of Various Machine Learning Techniques in Sentiment Analysis for Depression Detection

2019 ◽  
Vol 8 (10S) ◽  
pp. 292-296
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Mental Disorder ◽  
Human Life ◽  
Geographic Location ◽  
Machine Learning Techniques ◽  
World Health ◽  
Detection Algorithms ◽  
Learning Techniques ◽  
Depression Detection

Depression is the world’s fourth leading disease and will be in the second in 2020 according to the statistics of World Health Organization.Depression affects many people irrespective of their age, geographic location, demographic or social position and more commonly affects females than males.Depression is a mental disorder which can impair many facets of human life. Though not easily detected it has intense and wide-ranging impressions. Although many researchers explored numerous techniques in predicting depression, still there is no improvement and the generations are facing higher rate of depression. It is believed that the depression detection algorithms can be more accurate and their performance can be better if they rely on artificial intelligence. On considering these factors, it is planned to perform a survey on the application of various machine learning techniques that have been used in the domain of sentimental analysis for depression detection.

Artificial Intelligence and Machine Learning-Based Advanced Depression Detection System

2022 ◽  
pp. 92-114
Author(s):  
Suvarna Patil ◽  
Neha More ◽  
Animesh Bhawtankar ◽  
Vishal Pratap Jagtap ◽  
Anjali Jadhav
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Detection System ◽  
Quality Life ◽  
Depression Detection ◽  
Depressed Person ◽  
The Stability ◽  
The Mind ◽  
State Depression ◽  
Detection Of Depression

Depression can be called a health issue that majorly affects the stability of the mind. Depression can also be called a mood disorder or mental illness that affects our mental state. Depression can affect a person mentally and physically. According to WHO, 264 million people in the world are affected because of depression. At its worst, depression can lead to suicide. About 8 million people commit suicide every year because of depression. It is not possible for one to live with depression, but if they get proper treatment at a right time, depression can be controlled and cured to help the person to live a quality life. To identify the level of depression of a person, we have to first identify the type of depression the patient is going through. The type of depression plays a very important role in determining the kind of treatment or help a depressed person needs by providing them various treatments. The authors propose a solution for detection of depression type and depression levels using advanced machine learning and artificial intelligence algorithms.

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