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

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dc.contributor.authorAyberkın, Doruk
dc.contributor.authorSebetci, Özel
dc.date.accessioned2026-02-28T11:57:49Z
dc.date.available2026-02-28T11:57:49Z
dc.date.issued2025
dc.departmentBayburt Üniversitesi
dc.description.abstractThis 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.
dc.identifier.doi10.17798/bitlisfen.1646155
dc.identifier.endpage1419
dc.identifier.issn2147-3129
dc.identifier.issn2147-3188
dc.identifier.issue3
dc.identifier.startpage1403
dc.identifier.trdizinid1351735
dc.identifier.urihttps://doi.org/10.17798/bitlisfen.1646155
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1351735
dc.identifier.urihttps://hdl.handle.net/20.500.12403/5232
dc.identifier.volume14
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.relation.ispartofBitlis Eren Üniversitesi Fen Bilimleri Dergisi
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_TR-Dizin_20260218
dc.subjectData Analysis
dc.subjectPublic Policies
dc.subjectMachine Learning and Socioeconomic Indicators
dc.subjectCognitive Outcomes of Education Policies
dc.subjectPlanning with IQ Estimation
dc.titleThe Effectiveness of Advanced Machine Learning Models in IQ Prediction in the Context of Education, Health, and Socioeconomic Indicators
dc.typeArticle

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