Forecasting Pediatric Medical Expenses using Machine Learning: A Case Study of the Toto Afya Card in Tanzania

Abstract

In Tanzania, the discontinuation of the "Toto Afya" insurance package in March 2023 highlighted the challenges of managing escalating medical costs and ensuring the sustainability of child health insurance schemes. This research aimed to predict children's medical expenses using machine learning (ML) algorithms to aid the National Health Insurance Fund (NHIF) in effective cost management. Data from 1,212 registered children, comprising 7 variables, was used to train and test four ML models: linear regression, random forest, XGBoost, and CatBoost. CatBoost emerged as the best-performing model with an accuracy score of 82.1%, followed by XGBoost (79.2%), Random Forest (77.3%), and Linear Regression (76.4%). Additionally, CatBoost and XGBoost achieved the highest F1 scores at 81%. These results underscore CatBoost's effectiveness in accurately predicting children's medical expenses, which is crucial for NHIF's cost management and sustainability of insurance schemes. By leveraging ML algorithms like CatBoost, NHIF can improve evidence-based decision-making for policymakers and healthcare providers, ultimately enhancing access to affordable healthcare for children. This approach supports NHIF's goals and contributes to better healthcare outcomes and sustainability.