Women and Child Nutrition Analysis System
I. S. Kurkute*1, Muntode Payal Agasti*2, Gaikwad Neha Ashok*3, Nehe Tejaswini Sharad*4, Bhamare Samiksha Kakaji*5
1*Professor, Dept. of Computer Technology, P.Dr.V.V.P. Institute of Technology and Engineering, Loni,
Maharashtra, India
2,3,4 Final year Diploma Student, P.Dr.V.V.P. Institute of Technology and Engineering, Loni,
Maharashtra, India
Abstract - An essential technique for improving children's nutrition outcomes has been recognized as empowering women in several situations. To further investigate the connection between women's empowerment and outcomes, such as children's nutrition, additional disaggregated analyses of empowerment indices constructed from routine surveys conducted across countries are required. To investigate the link between women's empowerment and the nutritional status of children, researchers analyzed data from five South-Central Asian nations' Demographic and Health Surveys. The Survey-based Women's Empowerment (SWPER) index was used to quantify empowerment based on three dimensions: attitude toward violence, social independence, and decision-making. We looked at the main and interaction impact of the SWPER domains and the women's wealth index to see if empowering poorer mothers had a different beneficial influence on the nutrition outcomes for their children. The z-scores for children's height-for-age, weight-for-age, and weight-for-height were used to measure these outcomes. In order to look at interaction effects, we utilized logistic regression and correlation marginal effects to test linear probability models and main effects. Important control factors were included in the analyses, which were cluster-adjusted and sample-weighted. So, this research takes advantage of data on women and children and analyzes it for signs of disease using machine learning models such as linear regression and correlation factors. It is decided to inform both women and parents of children about nutrition guidelines based on these two criteria.
Keywords: Linear Regression, Pearson Correlation, Malnutrition, Health parameters, Decision making, Health analysis.
