Analysis of Machine Learning Algorithms for Predicting Depression

Narayanrao, Purude Vaishali; Kumari, P. Lalitha Surya

doi:10.1109/iccsea49143.2020.9132963

Cited by 37 publications

(8 citation statements)

References 10 publications

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“…Twint is an advanced tool written in Python that allows for scraping Tweets from Twitter profiles without using Twitter's Application Programming Interface (API). This tool has been used in other similar researches, such as in Narayanrao and Kumari (2020), in which data extraction is performed to predict tweets that have depressive content, in addition to Chamby-Diaz and Bazzan (2019), where the detection of transport traffic events is performed using machine learning models.…”

Section: Methodsmentioning

confidence: 99%

Social and institutional presence of the Heads of Government of the Americas on Social Media

Mendonça¹,

Santos

Junior

et al. 2021

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This article examined the personal profiles of the Heads of Government of countries in South/North America and how they communicated with their audiences on institutional measures to contain COVID-19. Analyses were carried out on data collected from Twitter from November-2019 to November-2020. This study includes: i)quantitative analysis, measuring categories and emphases in the communication of tweets, retweets, likes, and comments on matters relevant to the pandemic; ii)qualitative analysis that allowed evaluating speeches to identify political interference and the effectiveness of communication at critical moments of the pandemic. It was possible to infer that each president has his singularities and understanding about Social Media’s use as a more direct communication tool with his audience. It was also found that successful communication is not directly proportional to the volume of messages on Twitter, but to socio-political aspects and institutional leadership that can make a difference in Social Media in combating COVID-19.

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Section: Methodsmentioning

confidence: 99%

Social and institutional presence of the Heads of Government of the Americas on Social Media

Mendonça¹,

Santos

Junior

et al. 2021

logeion

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Section: Literature Reviewmentioning

confidence: 99%

“…Narayanrao et al [12] researched on analyzing the machine learning algorithms for forecasting depression. Videos of the general public are recorded and utilized as input for identifying depression [12]. The facial emotions captured on video are recognized-besides, the expression changes depending on the poses, lighting, person, viewpoint, and sensors.…”

Section: Literature Reviewmentioning

confidence: 99%

“…2D feature vector is used to hold the features. [12] Traditional ML algorithms Forecasting depression The facial emotions captured on video are recognized [13] Analysing and predict depression using cutting-edge technology Predicted depression rate Participants' scores were calculated by adding the values associated with each question set. [14] Multiple machine learning methods…”

Section: Literature Reviewmentioning

confidence: 99%

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Forecasting Mental Stress Using Machine Learning Algorithms

Hossain¹,

Alazeb²,

Almudawi³

et al. 2022

Computers, Materials &Amp; Continua

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Depression is a crippling affliction and affects millions of individuals around the world. In general, the physicians screen patients for mental health disorders on a regular basis and treat patients in collaboration with psychologists and other mental health experts, which results in lower costs and improved patient outcomes. However, this strategy can necessitate a lot of buy-in from a large number of people, as well as additional training and logistical considerations. Thus, utilizing the machine learning algorithms, patients with depression based on information generally present in a medical file were analyzed and predicted. The methodology of this proposed study is divided into six parts: Proposed Research Architecture (PRA), Data Pre-processing Approach (DPA), Research Hypothesis Testing (RHT), Concentrated Algorithm Pipeline (CAP), Loss Optimization Stratagem (LOS), and Model Deployment Architecture (MDA). The Null Hypothesis and Alternative Hypothesis are applied to test the RHT. In addition, Ensemble Learning Approach (ELA) and Frequent Model Retraining (FMR) have been utilized for optimizing the loss function. Besides, the Features Importance Interpretation is also delineated in this research. These forecasts could help individuals connect with expert mental health specialists more quickly and easily. According to the findings, 71% of people with depression and 80% of those who do not have depression can be appropriately diagnosed. This study obtained 91% and 92% accuracy through the Random Forest (RF) and Extra Tree Classifier. But after applying the Receiver operating characteristic (ROC) curve, 79% accuracy was found on top of RF, 81% found on Extra Tree, and 82% recorded for the eXtreme Gradient Boosting (XGBoost) algorithm. Besides, several factors are identified in terms of predicting depression through statistical data analysis. Though the additional effort is needed to develop a more accurate model, this model can be adjustable in the healthcare sector for diagnosing depression.

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“…They cope up with online teaching Learning method, online exam patterns, admissions to higher studies and summer internships etc. MS. Purude Vaishali Narayanrao et al [25], include different approaches to predict heart attack, peer pressure and depression. The data collection mechanism includes questionnaires and surveys with different people, their social media posts, text messages and verbal communication and facial expressions.…”

Section: Investigating ML Techniques For the Prediction Of Bipolar Disordermentioning

confidence: 99%

Review on Machine Learning Techniques to predict Bipolar Disorder

Agnihotri¹

2021

Preprint

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<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>

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Analysis of Machine Learning Algorithms for Predicting Depression

Cited by 37 publications

References 10 publications

Social and institutional presence of the Heads of Government of the Americas on Social Media

Social and institutional presence of the Heads of Government of the Americas on Social Media

Forecasting Mental Stress Using Machine Learning Algorithms

Review on Machine Learning Techniques to predict Bipolar Disorder

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