ILIOU machine learning preprocessing method for depression type prediction

Iliou, Theodoros; Κωνσταντοπούλου, Γεωργία; Ntekouli, Mandani; Lymperopoulou, Christina; Assimakopoulos, Konstantinos; Galiatsatos, Dimitrios; Anastassopoulos, George

doi:10.1007/s12530-017-9205-9

Cited by 30 publications

(12 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In 2019, Iliou et al [45] developed a data processing method for detecting depression. They considered a group of 249 students.…”

Section: 1rq1mentioning

confidence: 99%

See 1 more Smart Citation

A review and meta-analysis of machine intelligence approaches for mental health issues and depression detection

Chahar¹,

Dubey²,

Narang³

2021

IJATEE

View full text Add to dashboard Cite

“…In 2019, Iliou et al [45] developed a data processing method for detecting depression. They considered a group of 249 students.…”

Section: 1rq1mentioning

confidence: 99%

“…One-and four-times recording was performed for the participants with an interval of two weeks. Office of the Special Consulting Health Services, of Patras University[45]…”

mentioning

confidence: 99%

A review and meta-analysis of machine intelligence approaches for mental health issues and depression detection

Chahar¹,

Dubey²,

Narang³

2021

IJATEE

View full text Add to dashboard Cite

“…The key contribution of this paper is that the ILIOU preprocessing method can be utilized to significantly improve the performance of classification algorithms in similar datasets and it can also be used to forecast different types of depression. Depression prediction is critical for patients to receive the most appropriate treatment as soon as feasible [3]. Artificial intelligence is becoming a bigger aspect of medicine, and it will help with mental health research and practice.…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting Mental Health Illness using Machine Learning Algorithms

Vaishnavi

Kamath

Rao

et al. 2022

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

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%.

show abstract

“…Critical commitment of this paper is that ILIOU pre-processing technique can be used to fundamentally work on presentation of order calculations in comparative datasets and it can likewise be utilized to gauge various sorts of misery. Wretchedness expectation is basic for patients to get most suitable treatment when practical [3]. Man-made reasoning is turning into a greater part of medication, and it will assist with emotional well-being exploration and practice.…”

Section: Literature Reviewmentioning

confidence: 99%

Machine learning approaches for mental health diagnosis

Singh

2022

ijhs

View full text Add to dashboard Cite

Life expectancy may be greatly improved by accurately diagnosing mental health issues at an early stage. The goal of this study is to develop strong modelling tools for mental health clinical practise by using machine learning methods. An individual's risk of developing dementia, anxiety, depression, and other mental health issues will be assessed using machine learning techniques (e.g. genetics, cognition, demographics). Mental health problems may be treated more successfully when they are discovered early, which benefits both patients and the professionals who treat them. A person's psychological, emotional, and social well-being are all part of what is meant by mental health. It alters one's thoughts, feelings, and actions. From infancy and youth through maturity and beyond, good mental health is critical. The accuracy of five machine learning algorithms in detecting mental health disorders was examined in this research. Logistic regression, K-NN classifier, decision tree classifier, random forest, and stacking are the five machine learning approaches. We've tested these methods and applied them, and we've found the most accurate one out of all of them.

show abstract

ILIOU machine learning preprocessing method for depression type prediction

Cited by 30 publications

References 16 publications

A review and meta-analysis of machine intelligence approaches for mental health issues and depression detection

A review and meta-analysis of machine intelligence approaches for mental health issues and depression detection

Predicting Mental Health Illness using Machine Learning Algorithms

Machine learning approaches for mental health diagnosis

Contact Info

Product

Resources

About