Authors : Md. Azmain Yakin Srizon

Volume/Issue : Volume 10 - 2025, Issue 6 - June

Google Scholar : https://tinyurl.com/mrfcbuwm

DOI : https://doi.org/10.38124/ijisrt/25jun1203

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : This paper employs a novel approach by integrating machine learning techniques to quantitatively assess the impact of women's roles on family health outcomes. Through the analysis of data from 284 families, encompassing both urban and rural settings, this study highlights the pivotal role of women in healthcare decision-making processes and their significant contribution to promoting healthier family lifestyles. Key findings from the exploratory data analysis reveal that families with women holding Bachelor's degrees or higher exhibit markedly better health outcomes, underscoring the critical link between women's education and family health. Furthermore, the study identifies a positive correlation between women's participation in health-related discussions and the family's overall satisfaction with healthcare services. Most notably, the analysis demonstrates that women's influence in healthcare decisions significantly correlates with a lower incidence of chronic diseases within families. Machine learning analysis further substantiates these observations, pinpointing critical factors such as the educational attainment of women, their engagement in health discussions, and their active participation in managing the family's health budget as significant predictors of improved health outcomes. These findings underscore the substantial role of women in family health dynamics and advocate for targeted interventions to empower women in their roles as key agents of health within families. The study contributes valuable insights into the intersection of public health, gender studies, and data science, offering evidence-based recommendations for policy formulation and health interventions focused on leveraging the role of women to enhance family health outcomes.

Keywords : Machine Learning, Social Science Research, Women Empowerment, Exploratory Data Analysis, Comparative Analysis, Feature Selection, Machine Learning Classifiers.

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