Exploring Sleep Disorder and Lifestyle Analysis Through Data Preprocessing and Ensemble Learning Techniques

The health and well-being of people are being negatively affected by the rising prevalence of sleep problems in today's culture. In order to understand the intricate relationship between lifestyle variables and sleep problems, this research fills a crucial demand for efficient analytical methods. This research seeks to shed light on the complex links between lifestyle choices and sleep patterns by using advanced approaches such as data pre-processing, decision tree, random forest classifiers, and ensemble learning. The suggested method cleanses and prepares diverse datasets for analysis through extensive data preparation. Decision Tree (DT) and Random Forest (RF) classifiers are used to find patterns in the data and discover important lifestyle factors that affect sleep problems. In addition, by merging many classifiers, ensemble learning approaches like GridSearchCV and Voting Classifier are used to improve the model's forecast accuracy. This technique shows promise in accurately assessing and predicting sleep disorders based on lifestyle variables, as demonstrated by empirical evaluation on real-world datasets. The study's results shed light on the complex nature of sleep problems and provide useful information for healthcare providers and individuals seeking to improve the quality of their sleep and general health through customized therapies.