ItemConference Paper

Machine-Learning Based Sleep Pattern Analysis Using Linear Regression Algorithm

Title
Machine-Learning Based Sleep Pattern Analysis Using Linear Regression Algorithm
Creator
Reddy, Gudisa Vathsalya; Meghna, P.; Ramesh, Nandana; Jayapandian, Natarajan
Description
This article is investigating the connection between sleep patterns and concentration spans among university students while exploring the potential influence of MyersBriggs Type Indicator (MBTI) personality types on these aspects. The primary objective is to understand how sleep duration affects students ability to maintain focus and how their personality traits might interact with this relationship. Data was collected from university students aged 1619 using a multiple-choice form. The key variables analyzed were age, MBTI personality types, sleep duration, concentration span, and effective study ranking. Pearson's correlation was employed to examine these relationships. Additionally, a linear regression model was developed to predict concentration span based on sleep hours. The findings revealed a strong positive correlation 0.758 between sleep duration and concentration span, suggesting that increased sleep is associated with longer concentration spans. A moderate positive relationship 0.249 was also observed between concentration span and effective study ranking. However, the analysis showed a negligible relationship ? 0.008 between MBTI personality types and concentration span, indicating that within the context of this study, personality type does not significantly influence concentration span. This research emphasizes the critical role of sleep in academic settings and challenges the assumption that personality types significantly impact concentration span and sleep patterns. The linear regression model developed provides a predictive tool for understanding the impact of sleep on concentration, underscoring the importance of adequate sleep for academic success. This research is contributing to the broader understanding of factors influencing student performance and offers practical insights for optimizing study habits and educational strategies. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Source
Lecture Notes in Electrical Engineering;Volume;1420 LNEE;pp.131-142
Date
01-01-2025
Publisher
Springer Science and Business Media Deutschland GmbH
Subject
Concentration span; Linear regression; Machine learning; MyersBriggs type indicator; Sleep pattern
Coverage
Reddy G.V., Department of Computer Science and Engineering, Christ University, Kengeri Campus, Bangalore, India; Meghna P., Department of Computer Science and Engineering, Christ University, Kengeri Campus, Bangalore, India; Ramesh N., Department of Computer Science and Engineering, Christ University, Kengeri Campus, Bangalore, India; Jayapandian N., Department of Computer Science and Engineering, Christ University, Kengeri Campus, Bangalore, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 18761100; ISBN: 978-981966405-4;
Format
online
Language
English
Type
Conference paper

Citation

Reddy, Gudisa Vathsalya; Meghna, P.; Ramesh, Nandana; Jayapandian, Natarajan, “Machine-Learning Based Sleep Pattern Analysis Using Linear Regression Algorithm,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 20, 2026, https://archives.christuniversity.in/items/show/25588.

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