Proposed Technological Solution to Predict the Need for Health Professionals in Health Centers Using Random Forest

The objective of this research is to develop a technological solution based on the Random Forest algorithm to predict healthcare workforce requirements in public healthcare centers in Peru, addressing staff shortages and unequal workforce distribution. A national dataset from the Peruvian Ministry of Health (MINSA) covering the period 2017–2024, segmented by levels of care (I, II, and III), was used to capture the operational differences within the healthcare system. The model, validated using an 80/20 split, achieved outstanding performance, with coefficients of determination (R²) exceeding 0.99 and minimal percentage errors (MAPE) across all levels of care. The main contribution of this work lies in converting estimated healthcare attendances into an operational metric of “required healthcare professionals”, integrated into a web-based architecture built on React, Flask, and PostgreSQL. The findings identify medical specialty and year as the most influential predictive variables. It is concluded that the proposed tool is robust for optimizing strategic healthcare workforce planning, enabling a more equitable and data-driven allocation of medical specialists.