Machine learning-enabled classification of global human development using INFORM risk indicators

Year 2026, Volume: 6 Issue: 1, 1 - 15

Merve Doğruel

Abstract

This study aims to identify the most effective machine learning model for classifying countries' Human Development Index (HDI) levels using indicators from the INFORM Risk Index. The motivation for this work lies in the growing need for data-driven methods to analyze and predict human development outcomes, particularly in the context of complex and high-dimensional socio-economic and disaster-related risk data. Traditional models often fail to capture the non-linear relationships that influence human development. To address this gap, six supervised machine learning algorithms—k-Nearest Neighbors (KNN), Linear and Nonlinear Support Vector Machines (SVM), Classification and Regression Trees (CART), Bagging, and Random Forest (RF)—were systematically evaluated. Performance was measured using weighted F1-scores on both training and testing datasets. The results reveal that while KNN, Linear SVM, and CART have limited predictive power, the Nonlinear SVM suffers from overfitting. In contrast, ensemble-based models—Bagging and RF—demonstrate superior and balanced performance, with F1-scores around 0.80 on both datasets. These methods also allow for interpretability through feature importance analysis. Socio-economic, institutional, and infrastructure-related indicators were identified as the most influential variables in predicting HDI levels. The findings highlight the strength of ensemble learning in modeling complex development-related risks and provide a robust framework for integrating machine learning into global human development analysis. This study offers valuable insights for policymakers and researchers aiming to improve forecasting, resilience planning, and development strategies.

Keywords

Machine learning , Bagging , Random forest , Human development index , INFORM risk

INFORM risk göstergeleriyle makine öğrenimi tabanlı küresel insan gelişimi sınıflandırması

Abstract

Bu çalışma, gelişmiş makine öğrenmesi tekniklerinden yararlanarak, ülkelerin İnsani Gelişme Endeksi (HDI) seviyelerinin INFORM risk göstergeleri kullanılarak sınıflandırılması için en uygun kestirim modelini araştırmaktadır. Altı farklı sınıflandırma algoritması—k-En Yakın Komşu (k-NN), Doğrusal ve Doğrusal Olmayan Destek Vektör Makineleri (SVM), Sınıflandırma ve Regresyon Ağaçları (CART), Bagging ve Rastgele Orman (RF)—sistematik olarak değerlendirilmiştir. Model performansları, hem eğitim hem de test veri setleri üzerinde hesaplanan ağırlıklı F1 skoru aracılığıyla titizlikle değerlendirilmiş ve yöntemler arasında anlamlı farklılıklar ortaya konmuştur. KNN, Doğrusal SVM ve CART, görece sınırlı kestirim doğruluğu sergilerken, Doğrusal Olmayan SVM, eğitim setinde yüksek başarı gösterip test setinde performans düşüşü yaşamasıyla aşırı öğrenme (overfitting) belirtileri göstermiştir.
Buna karşılık, Bagging ve Rastgele Orman gibi topluluk (ensemble) yöntemleri, hem eğitim hem de test veri setlerinde yaklaşık 0.80 düzeyinde dengeli ve yüksek F1 skorları ile tutarlı biçimde üstün performans sergilemiş, bu da onların sağlamlığını ve güçlü genelleme yeteneklerini ortaya koymuştur.
Elde edilen bulgular, gelişim araştırmalarında yüksek boyutlu ve karmaşık sosyo-ekonomik verilerin işlenmesinde topluluk öğrenme tekniklerinin etkinliğini açık biçimde desteklemektedir.
Ayrıca yapılan özellik önem analizi, sosyo-ekonomik, kurumsal ve altyapıya ilişkin değişkenlerin HDI kestirimi üzerinde belirleyici etkiler yarattığını göstermektedir. Bu çalışma, kapsamlı karşılaştırmalı değerlendirmeyi yorumlanabilirlik analizleri ile birleştirerek, topluluk makine öğrenmesi yaklaşımlarının sosyo-ekonomik risk değerlendirmeleri ve insani gelişme öngörüleri için uygulanabilirliğine dair ampirik kanıt sunmaktadır. Elde edilen içgörüler, kalkınma süreçlerini izlemeye ve iyileştirmeye yönelik çalışan politika yapıcılar ve araştırmacılar için veri odaklı değerli bir çerçeve sunmakta ve makine öğrenmesinin sosyo-ekonomik araştırma alanlarındaki dönüştürücü potansiyelini vurgulamaktadır.

Keywords

Makine öğrenmesi , Bootstrap toplulaştırması , Rastgele orman , İnsani gelişmişlik endeksi , INFORM Risk Endeksi

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