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Journals / Hacettepe Sağlık İdaresi Dergisi / 2026 / Cilt: 29 Sayı: 3

ANALYZING INEQUALITIES IN ECONOMIC DEVELOPMENT, HEALTH, AND ENVIRONMENT WITH MACHINE LEARNING

MAKİNE ÖĞRENİMİ İLE EKONOMİK KALKINMA, SAĞLIK VE ÇEVRE EŞİTSİZLİKLERİNİN ANALİZİ

Gülsüm Şeyma Koca(KASTAMONU ÜNİVERSİTESİ, SAĞLIK BİLİMLERİ FAKÜLTESİ, SOSYAL HİZMET BÖLÜMÜ)0000-0002-1356-3975
Nazan Kartal(ÇANKIRI KARATEKİN ÜNİVERSİTESİ, SAĞLIK BİLİMLERİ FAKÜLTESİ)0000-0002-5416-7952
İlkay Sevinç Turaç(KİLİS 7 ARALIK ÜNİVERSİTESİ, YUSUF ŞEREFOĞLU SAĞLIK BİLİMLERİ FAKÜLTESİ)0000-0003-2884-8049
Pages
647–660
Publication Date
2026-09-23
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Reducing air pollution and increasing access to clean water stand out as critical strategies for mitigating health inequalities.

📌 3 Key Takeaways

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Objective & Hypothesis:Inequalities in health and environmental sustainability persist among countries with varying levels of economic development.
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Key Findings & Data:Furthermore, the study seeks to identify the key determinants of life expectancy across income groups and to analyze disparities in maternal mortality rates, diabetes prevalence, PM2.5 air pollution, renewable energy consumption, and internal freshwater resources.
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Conclusion & Impact:Reducing air pollution and increasing access to clean water stand out as critical strategies for mitigating health inequalities.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Sağlık eşitsizlikleri · Çevresel Sürdürülebilirlik · Makine Öğrenimi

Abstract

Inequalities in health and environmental sustainability persist among countries with varying levels of economic development. This study examines the relationships between economic classifications and life expectancy by analyzing key health and environmental indicators across countries. The findings aim to assist policymakers in enhancing access to healthcare services, developing targeted interventions, and fostering sustainable development. The primary objective is to evaluate the associations between income levels and indicators of health and environmental sustainability using predictive modeling techniques. Furthermore, the study seeks to identify the key determinants of life expectancy across income groups and to analyze disparities in maternal mortality rates, diabetes prevalence, PM2.5 air pollution, renewable energy consumption, and internal freshwater resources. Data from 40 countries—categorized into low-, lower-middle-, upper-middle-, and high-income groups according to World Bank classifications—were used. The independent variables include maternal mortality rate, life expectancy at birth, diabetes prevalence, PM2.5 air pollution, renewable energy consumption, and renewable internal freshwater resources per capita. Decision Tree and Random Forest algorithms were employed to classify countries by income group, and analyses were conducted using Orange data mining software. The decision tree model identified life expectancy at birth as the strongest discriminating variable. Maternal mortality rate was found to be significant in distinguishing low-income groups, while renewable internal freshwater resources proved important for classifying income groups. The findings demonstrate that economic status is a decisive factor influencing health and environmental outcomes. Policies focusing on reducing maternal mortality in low-income countries and combating non-communicable diseases in high-income countries are of importance. Reducing air pollution and increasing access to clean water stand out as critical strategies for mitigating health inequalities.

Özet

Sağlık ve çevresel sürdürülebilirlik alanındaki eşitsizlikler, farklı ekonomik gelişmişlik düzeylerine sahip ülkeler arasında devam etmektedir. Bu çalışma, ülkelerin temel sağlık ve çevresel göstergelerini analiz ederek ekonomik sınıflandırmalar ile yaşam beklentisi arasındaki ilişkileri incelemektedir. Elde edilen bulguların politika yapıcılara sağlık hizmetlerine erişimi artırmaya, hedefli müdahaleler geliştirmeye ve sürdürülebilir kalkınmayı desteklemeye yönelik katkı sağlaması amaçlanmaktadır. Çalışmanın temel amacı, gelir düzeyleri ile sağlık ve çevresel sürdürülebilirlik göstergeleri arasındaki ilişkileri öngörücü modelleme teknikleri kullanılarak değerlendirmektir. Ayrıca, farklı gelir gruplarında yaşam beklentisini etkileyen başlıca belirleyicileri ortaya koymak ve anne ölüm oranı, diyabet prevalansı, PM2.5 hava kirliliği, yenilenebilir enerji tüketimi ile iç tatlı su kaynaklarındaki eşitsizlikleri incelemektir. Çalışmada, Dünya Bankası sınıflandırmasına göre düşük, düşük-orta, üst-orta ve yüksek gelir gruplarında yer alan 40 ülkeye ait veriler kullanılmıştır. Bağımsız değişkenler anne ölüm oranı, doğumda yaşam beklentisi, diyabet prevalansı, PM2.5 hava kirliliği, yenilenebilir enerji tüketimi ve kişi başına yenilenebilir iç tatlı su kaynaklarıdır. Ülkelerin gelir gruplarına göre sınıflandırılmasında Karar Ağacı ve Rastgele Orman algoritmaları kullanılmış; analizler Orange veri madenciliği yazılımı kullanılarak gerçekleştirilmiştir. Karar ağacı modelinde en güçlü ayırt edici değişkenin doğumda beklenen yaşam süresi olduğu belirlenmiştir. Anne ölüm oranının düşük gelir gruplarını ayırt etmede, yenilenebilir iç tatlı su kaynaklarının ise gelir gruplarının sınıflandırılmasında önemli olduğu görülmüştür. Bulgular ekonomik statünün sağlık ve çevresel sonuçlar üzerinde belirleyici olduğunu göstermektedir. Düşük gelirli ülkelerde anne ölümlerini azaltmaya, yüksek gelirli ülkelerde ise bulaşıcı olmayan hastalıklarla mücadeleye odaklanan politikalar önem taşımaktadır. Hava kirliliğinin azaltılması ve temiz suya erişimin artırılması, sağlık eşitsizliklerinin azaltılmasında kritik stratejiler olarak öne çıkmaktadır.

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Cite This Article

Koca, G. Ş., Kartal, N., Turaç, İ. S. (2026). ANALYZING INEQUALITIES IN ECONOMIC DEVELOPMENT, HEALTH, AND ENVIRONMENT WITH MACHINE LEARNING. Hacettepe Sağlık İdaresi Dergisi, Cilt: 29 Sayı: 3, 647–660. https://doi.org/10.61859/hacettepesid.1885340

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