Generation Z Health Trends: Data Mining of Sleep and Lifestyle Patterns

Abstract

This study analyzes sleep health and lifestyle data among Generation Z using RapidMiner to identify key issues such as insomnia and stress within this demographic. The research is motivated by the increasing concerns surrounding sleep health and lifestyle choices that significantly impact overall well-being. The primary objective is to identify patterns and correlations in health data to inform targeted interventions and lifestyle improvements. The methodology involves employing RapidMiner to process and analyze a dataset comprising variables including gender, age, occupation, sleep duration, sleep quality, physical activity level, stress level, BMI category, blood pressure, heart rate, daily step count, and the presence of sleep disorders. Key data mining techniques such as classification and association are utilized to extract meaningful insights. Classification is applied to predict patterns in sleep health and association analysis uncovers relationships between variables. The analysis reveals significant findings: individuals with poor sleep quality and high-stress levels often exhibit lower physical activity and imbalanced BMI, indicating potential health risks. The results provide a comprehensive understanding of the sleep health and lifestyle trends among Generation Z, highlighting critical areas for improvement. These insights contribute to the development of tailored health programs, enabling policymakers and healthcare providers to design interventions that promote better sleep hygiene and healthier lifestyles. This research underscores the utility of data mining tools like RapidMiner in addressing contemporary health challenges.