Abstract:
Global populations are significantly impacted by
insomnia, a prevalent sleep problem that negatively impacts
daily functioning and general well-being. The intricacies of
insomnia are explored in this study by utilizing a large dataset
that includes both self-reported tests and thorough
questionnaires covering various topics, including sleep habits,
stress levels, early life events, and cognitive impairments. The
study's main objectives are finding relevant components,
examining correlations, and utilizing predictive modeling
approaches to reveal important insights. We used advanced
feature selection techniques to understand the complex
interactions between variables. This study examined the
intricacies of insomnia's effects on adolescents utilizing a range
of statistical metrics, including correlation coefficients and pvalues. P-values, which show how significant the observed links
are, and correlation coefficients, which show how strong and
which way the relationships are going, are important metrics
in our analysis. Using a variety of machine learning methods,
such as Decision Trees (DT), k-Nearest Neighbors (k-NN),
Support Vector Machines (SVM), and Naive Bayes (NB), one
of the study's main goals was to predict insomnia-related
outcomes. Among the models evaluated, the Decision Tree
classifier was the most accurate, with an exceptional accuracy
rate of 89.47% for both feature selection strategies. These
results highlight how reliable Decision Trees are at identifying
patterns of sleeplessness. Additionally, the investigation found
statistically significant correlations between particular
demographic characteristics and insomnia. An
important positive link between sex and insomnia was found,
with a correlation coefficient of 0.078 and a p-value of 0.001.
Age and insomnia showed a significant positive link
(correlation coefficient = 0.250). However, the p-value of 0.553
suggests that more research is needed to understand this
relationship fully. Further supporting the need to consider
these factors for a thorough understanding and management of
insomnia, the study found significant correlations between race
(correlation coefficient = 0.05, p-value = 0.0) and ethnicity
(correlation coefficient = 0.179, p-value = 0.716) with insomnia
