Comparing Multivariate Adaptive Regression Splines and Machine Learning Methods for Classifying Pneumonia in Indonesian Toddlers - Ardi Kurniawan; Nur Azizah; Sheila Sevira Asteriska Naura (Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang, 2025)
Pneumonia is a type of infectious and contagious respiratory disease that causes death in toddlers. According to the Indonesian Ministry of Health (2024), the coverage of pneumonia among toddlers in 2023 was 36.95% with a total of 416,435 cases. This study aims to model and classify the pneumonia status of toddlers in Indonesia using the Multivariate Adaptive Regression Splines (MARS) method and several machine learning methods, such as logistic regression, K-NN, random forest, and SVM. This study uses secondary data from the Survei Kesehatan Indonesia in 2023 and the Profil Kesehatan Indonesia in 2023, including variables such as the percentage of toddlers health service coverage, low birth weight babies, population density, percentage of malnutrition in toddlers, prevalence of smoking in the population aged ≥10 years in the last 1 month, percentage of toddlers who are exclusively breastfed, and percentage of toddlers who have incomplete basic immunization. The best model obtained using the MARS method is with BF = 14, MI = 2, and MO = 3. This model produces a GCV value of 0.122 and R-Square of 82.9%, which shows good prediction performance. The classification results show that the MARS method is superior to the logistic regression, K-NN, random forest, and SVM methods with an accuracy rate of 97.06%.