2023
DOI: 10.1016/j.heliyon.2023.e18550
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Fuzzy clustering algorithm for university students' psychological fitness and performance detection

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Cited by 8 publications
(3 citation statements)
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“…[55][56][57][58] These mental health problems can affect a K-12 student's energy level, concentration, optimism, mental capability, and dependability, which can hinder his or her academic performance. 59 We found that students with high household incomes, students with higher intelligence quotients, and students who read books are more likely to have good academic performance. Our results are similar to previous studies.…”
Section: Discussionmentioning
confidence: 69%
See 1 more Smart Citation
“…[55][56][57][58] These mental health problems can affect a K-12 student's energy level, concentration, optimism, mental capability, and dependability, which can hinder his or her academic performance. 59 We found that students with high household incomes, students with higher intelligence quotients, and students who read books are more likely to have good academic performance. Our results are similar to previous studies.…”
Section: Discussionmentioning
confidence: 69%
“… 55–58 These mental health problems can affect a K-12 student’s energy level, concentration, optimism, mental capability, and dependability, which can hinder his or her academic performance. 59 …”
Section: Discussionmentioning
confidence: 99%
“…The machine learning methods offer different advantages in evaluating the mental health of students, including early detection, objectivity, personalization, scalability, and resource efficiency, however, they have several challenges such as quality, interpretability, slow performance with a large number of datasets [50], limitations, such as their need for a large amount of data [51], reproducing model solution [52], and stochastic graph problems [53]. Additionally, the heuristic fuzzy c-means clustering algorithm struggles with limited dataset sizes [54]. Addressing these challenges involves careful data collection, interdisciplinary collaboration, robust evaluation techniques, and validation to maximize the benefits and minimize the risks of using machine learning in student mental health assessment.…”
Section: Related Workmentioning
confidence: 99%