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Dergiler / Bitlis Eren Üniversitesi Fen Bilimleri Dergisi / 2026 / Volume: 14 Issue: 3

The Effectiveness of Advanced Machine Learning Models in IQ Prediction in the Context of Education, Health, and Socioeconomic Indicators

Doruk Ayberkin(BAYBURT ÜNİVERSİTESİ, UYGULAMALI BİLİMLER FAKÜLTESİ, YÖNETİM BİLİŞİM SİSTEMLERİ BÖLÜMÜ, YÖNETİM BİLİŞİM SİSTEMLERİ PR.)0000-0003-3409-8926
Özel Sebetci(ADNAN MENDERES UNIVERSITY)0000-0002-2996-0270
Sayfa
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Yayın Tarihi
2025-09-30
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

The findings provide valuable insights for policymakers and researchers, suggesting that machine learning approaches can significantly enhance understanding of the determinants of IQ and aid in developing targeted strategies in education and social policy.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:This study investigates the effectiveness of advanced machine learning models in predicting IQ levels using a diverse set of socioeconomic and health indicators from global databases such as WHO, the World Bank, and United Nations organizations.
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Temel Bulgular & Veri:The research employs various algorithms, including Linear Regression, Random Forest, Gradient Boosting, Support Vector Machines, Ridge and Lasso Regressions, XGBoost, LightGBM, and a Stacking Regressor to capture both linear and non-linear relationships.
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Sonuç & Katkı:The findings provide valuable insights for policymakers and researchers, suggesting that machine learning approaches can significantly enhance understanding of the determinants of IQ and aid in developing targeted strategies in education and social policy.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Machine Learning and Socioeconomic Indicators · Data Analysis · Public Policies

Abstract

This study investigates the effectiveness of advanced machine learning models in predicting IQ levels using a diverse set of socioeconomic and health indicators from global databases such as WHO, the World Bank, and United Nations organizations. The research employs various algorithms, including Linear Regression, Random Forest, Gradient Boosting, Support Vector Machines, Ridge and Lasso Regressions, XGBoost, LightGBM, and a Stacking Regressor to capture both linear and non-linear relationships. Evaluation metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared (R²) reveal that LightGBM and Stacking Regressor models excel in accuracy and generalization. The study highlights the trade-off between model interpretability and predictive power, emphasizing that simpler models offer greater transparency. In contrast, more complex models successfully capture intricate interactions among education, health, and economic factors. The findings provide valuable insights for policymakers and researchers, suggesting that machine learning approaches can significantly enhance understanding of the determinants of IQ and aid in developing targeted strategies in education and social policy.

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Bu Makaleye Atıf Yap

Ayberkin, D., Sebetci, Ö. (2025). The Effectiveness of Advanced Machine Learning Models in IQ Prediction in the Context of Education, Health, and Socioeconomic Indicators. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, Volume: 14 Issue: 3. https://doi.org/10.17798/bitlisfen.1646155

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