Abstract:
Addiction to digital technology has become a major issue in modern culture, affecting
mental health and wellbeing in a variety of intricate ways. On the one hand, excessive
screen time and technology use can cause a number of negative effects, such as worsening
attention-deficit symptoms, lowering social and emotional intelligence, developing a
technology addiction, and isolating oneself from others. Notable side effects include
disturbed sleep patterns and a deterioration in psychological well-being, which are made
worse by less social interactions, a rise in hopelessness and loneliness, and a drop in selfworth. Moreover, there is a negative relationship between internet addiction and subjective
well-being, which includes good emotions and life satisfaction. The significance of
processing capacity, data storage, and reliable networking gear as hardware requirements
for managing sizable datasets and executing complex machine learning algorithms is
highlighted by this study. The process includes gathering a lot of data via questionnaires,
interviews, and surveys. Metrics like the F1 score are used in performance assessment and
are especially useful for skewed datasets. The investigation of several machine learning
models, such as Decision Trees, Support Vector Machines, K-Nearest Neighbors, Logistic
Regression, Random Forest, Gradient Boosting, XGBoost, CatBoost, Neural Network, and
XGBoost3, reveals the effectiveness of these models in predicting the impact of digital
technology addiction on mental health. Of these, Gradient Boosting and Neural Network
models demonstrated the highest accuracy in modeling the complex relationships between
digital technology use and mental health outcomes. On the other hand, the study recognizes
the potential for digital technology to influence behavioral issues and disorders, such as
ADHD. This research offers insights into the possible advantages and disadvantages of
using digital technology, as well as a thorough review of the effects of addiction to digital
technology on mental health and wellbeing
