A Comparative Study of Clustering Humans Based on Their Stress Level

Stress is a state of mental or emotional weariness brought on by unavoidable or challenging situations. Additionally, the caliber of sleep has a crucial role in physical and mental health. Research on stress is interesting and beneficial to society, since it a hot issue. This study’s main objective is to investigate how humans are grouped based on their stress levels. The stress level is measured based on sleeprelated activities. To estimate the amount of stress, we applied machine learning (ML) algorithms. We made use of secondary information from the Kaggle website. The dataset includes seven sleeping habits and stress levels. The data set has been divided into two categories: low and high stress. We grouped humans based on their stress levels by considering their sleeping habits. After preprocessing the data, the findings are then acquired utilizing three unsupervised ML algorithms for clustering humans based on their stress level. We used Expectation Maximum (EM) methods, Simple KMeans, and Hierarchical Clustering as three different clustering algorithms to analyze the cluster. The Simple KMeans approach successfully categorized data with high accuracy, recall, f-measure values, and precision, according to results acquired using a data mining tool. According to the evaluation results, the Simple KMeans clustering method outperformed the outcomes of the other two algorithms with the accuracy 93.25%. The model can utilize to measure human stress and the connection between human stress and sleep were determined at the conclusion of the study.