Context:

One of the clients struggled with high readmission rates for chronic disease patients. Manual tracking made it difficult to identify high risk individuals, leading to increased costs and poor patient outcomes. Our team understood this case and investigated this situation.

Resolution:

We deployed a specialized team with deep knowledge and used R to build a predictive model using logistic regression and random forest algorithms. Packages like caret, dplyr, and ggplot2 helped clean data, engineer features, and visualize risk patterns.

Result:

The model accurately identified high risk patients with over 80 percent precision. Early intervention programs reduced readmissions by 25 percent, improving patient care and lowering operational costs.