Analyzing the Health Status of People in Rural Areas Using Machine Learning Algorithms

This approach presents an overview of how AI is revolutionizing public health, creating opportunities for those from a low-income status to acquire healthcare that will be better, faster, and fairer. This article will address grave health disparities in neglected areas by using machine learning algorithms to investigate the health status of rural communities. The most efficacious method of anticipating health results was determined through a comparison of four different models: logistic regression, random forest, support vector machines, and gradient boosting. Gradient Boosting is an excellent competitor for health assessments as its AUC-ROC score is significantly higher than the other models. This proposed method will aid in the improvement of health interventions in the underprivileged rural areas by estimating the crucial health indicators and thus is a precious resource for early detection and monitoring. These results indicate that machine learning can contribute to improving public health infrastructure and healthcare service delivery.