Comprehensive Exploratory Analysis of Stroke Risk Factors: A Statistical Exposition
Nguyen Minh Tuan
Journal of Information Organization, Vol. 16, No. 2, 2026.
DOI: https://doi.org/10.6025/jio/2026/16/2/78-93
Link: https://www.dline.info/jio/fulltext/v16n2/jiov16n2_3.pdf
Abstract:
Stroke remains a leading cause of global mortality and long term disability, necessitating reliable predictive
frameworks for early risk identification. While machine learning applications in stroke forecasting have
expanded, many studies bypass rigorous exploratory data analysis, compromising model interpretability
and clinical generalizability. This study presents a comprehensive exploratory investigation of demographic,
clinical, and lifestyle determinants of stroke using a structured healthcare dataset of 5,110 patient records.
Through descriptive statistics, univariate and bivariate analyses, correlation mapping, and stratified
prevalence assessments, we systematically evaluated feature distributions, missingness patterns, and
multivariate relationships. The cohort exhibited severe class imbalance, with stroke-positive cases comprising
only 4.9% of observations. Age emerged as the strongest univariate predictor (r = 0.245), followed by preexisting
heart disease, elevated average glucose levels, and hypertension. Conversely, body mass index
demonstrated weak independent discriminative power, suggesting its influence operates indirectly through
metabolic and cardiovascular pathways. Pairwise visualizations and correlation heatmaps confirmed
minimal multicollinearity and highlighted clinically meaningful interaction effects between aging and
dysglycemia. These findings establish a statistically rigorous foundation for subsequent machine learning
development, emphasizing the necessity of imbalance aware evaluation metrics and targeted feature
engineering. By bridging raw clinical data and algorithmic deployment, this work provides a transparent,
reproducible roadmap to advance clinically actionable stroke risk stratification tools.