Multifactorial Risk Analysis and Prediction for Osteoporosis among Adults using Exploratory Data Analysis

Uma G, Sri Aishwarya A M

Abstract: Osteoporosis is a silent, progressive bone disease that significantly increases the risk of fractures and morbidity, particularly among aging populations. This study aims to analyse the multifactorial risk factors contributing to osteoporosis among adults using statistical methods. The dataset, sourced from Kaggle, comprises 1, 958 samples with variables spanning demographic (age, gender, race), lifestyle (physical activity, smoking, alcohol), nutritional (calcium and vitamin D intake), and medical (hormonal changes, medications, prior fractures) domains. Using Python and Power BI, the data is analysed with Exploratory Data Analysis (EDA), Chi - square test, and two - sample t - test to determine the descriptive measures, associations and patterns among the variables of the dataset. Results revealed age as the most significant predictor, with a higher prevalence observed in individuals aged 40 and above. Other contributing factors included underweight status, sedentary lifestyle, postmenopausal hormonal status, and corticosteroid medication use. However, variables such as family history and nutrition showed weak associations. This paper highlights the importance of early identification of at - risk individuals through basic demographic and lifestyle indicators to support preventive strategies.

Keywords: Osteoporosis, Risk Factors, Descriptive Statistics, EDA, Chi - Square Test, t - Test, Bone Health, Lifestyle Analysis

How to Cite?: Uma G, Sri Aishwarya A M, "Multifactorial Risk Analysis and Prediction for Osteoporosis among Adults using Exploratory Data Analysis", Volume 14 Issue 5, May 2025, International Journal of Science and Research (IJSR), Pages: 1156-1160, https://www.ijsr.net/getabstract.php?paperid=SR25517131726, DOI: https://dx.doi.org/10.21275/SR25517131726