Prediction of Mental Stress Level Based on Machine Learning

Kene, Akshada; Thakare, Shubhada

doi:10.1007/978-981-16-9650-3_41

Cited by 7 publications

(3 citation statements)

References 12 publications

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“…According to Mozgovoy [17], Kyamakya et al [18] and Garg et al [15], several studies have applied the main Machine Learning algorithms in dataset analysis and evaluated their performance through metrics established in the literature in order to indicate which algorithm produces better accuracy in its predictions. According to Ahuja and Banga [19], and Kene and Thakare [20], several studies have reported results with accuracy rates over 80% in the prediction of stress levels, which was considered a satisfactory result in this context. According to Priya et al [21], several researchers have applied different Machine Learning algorithms and obtained different accuracy rates depending on the scenario, which shows that a single algorithm is not the best to every situation, and that such studies have been conducted following an empirical approach.…”

Section: Datasets and Machine Learningmentioning

confidence: 84%

Using Machine Learning Techniques to Analyze Stress Levels in Electroencephalogram Data

Santos

2023

IJSREM

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Research in mental health has proposed the application of technologies that allow the analysis of substantial amounts of data and the provisioning of mechanisms to monitor and diagnose the health status of individuals with the objective of improving their quality of life. Mental stress has been widely addressed in this type of research as it is widely acknowledged as a condition that leads to negative impacts on modern life. Several studies have been directed to the generation of datasets with data from physiological signals obtained through sensors and the public availability of such datasets for further research. One of the main approaches for the processing and analysis of these datasets has been the use of Machine Learning techniques. This study explores the use of Machine Learning techniques to analyze a dataset consisting of mental stress level classification data from electroencephalograms. Different Machine Learning models were compared. The Multilayer Perceptron model presented the best performance with an accuracy rate of 98.99% in the predictions. The results demonstrate the potential of Machine Learning techniques as aiding tools to health monitoring and diagnosis. Key Words: machine learning, deep learning, mental stress, electroencephalogram

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Section: Datasets and Machine Learningmentioning

confidence: 84%

Using Machine Learning Techniques to Analyze Stress Levels in Electroencephalogram Data

Santos

2023

IJSREM

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“…Recent research on predicting student mental health conditions using machine learning algorithms has yielded a medium level of accuracy, and they lack in using more number of parameters or features, and also they have used very less number of samples. In the research work of (4) only 270 participants or samples were used, whereas compared to this research paper there are more than 6000 samples. Some other paper shows the usage of only one or two algorithms.…”

Section: Resultsmentioning

confidence: 99%

“…In (4) the study's author discussed earlier findings on machine learning-based stress detection research studies. Our system for identifying user stress is predicated on the random forest (RF) algorithm and support vector machine (SVM).…”

Section: • Purpose Of the Researchmentioning

confidence: 99%

Decoding Minds: Estimation of Stress Level in Students using Machine Learning

Shahapur,

Chitti,

Patil

et al. 2024

IJST

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Objectives: Develop a predictive model to categorize student’s stress levels and support early interventions based on self-reported data, academic performance, and study load. This will help to receive early diagnosis and treatment. Methods: In this work the data set used was downloaded from a website called KAGGLE. The dataset has more than 6000 samples, the parameters considered in this dataset are Anxiety level, self-esteem, mental_health_history, depression, headache, blood pressure, sleep_quality, breathing_problem, noise_level, living conditions, Safety, basic needs, academic performance, study_load, teacher_student_relationship, future_career_concerns, social support, peer_pressure, extracurricular_activities and bullying which directly or indirectly has an effect on the mental health of the students, so basically here 20 different types of factors are taken into consideration. This specific Research Work employs Machine Learning (ML) approaches to analyze stress levels in students from stress-level text data. Logistic Regression (LR) with 89.46%, KNeighbors with 92.8%, Decision Tree with 94.5%, Random Forest with 95%, and Gradient Boosting with 90.15%, algorithms are used to determine stress levels. Findings: Several significant findings have emerged in this research on predicting mental stress levels in students using machine learning. Studies on feature importance emphasize the importance of sleep quality, depression, mental_health_history, academic performance, and participation in extracurricular activities and several other parameters as critical criteria for accurate prediction. Multimodal techniques that integrate data from mental health history, family history, and academic records provide a more complete picture of a student’s life. Temporal dynamics are important, as stress levels fluctuate throughout time as a result of academic and personal events. Some research goes beyond prediction, investigating intervention options based on tailored stress management suggestions. Novelty: In order to anticipate student’s mental stress, this study presents a novel machine-learning architecture. This methodology attempts to give early identification of students’ mental health at risk by leveraging diverse data sources and using different machine learning algorithms with a very high accuracy level. Keywords: Stress Level, Students, Machine Learning, Decision Tree, Physio Bank

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