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

Doruk Ayberkin; Özel Sebetci

Makale detayları

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

Yazarlar:

Doruk Ayberkin | Özel Sebetci

Yayıncı:

Bitlis Eren University

Ana Başlıklar:

Statistics (Other) | Statistical Data Science

Dergi:

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi Vol.14 Issue 3, pp.1403-1419

Belge Tipi:

Makale

Yayın Tarihi:

9/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001359241

pISSN:

2147-3129

Anahtar kelimeler:

Özet:

 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.

 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.

 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.

 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.

Makale detayları

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

Yazarlar:

Doruk Ayberkin | Özel Sebetci

Yayıncı:

Bitlis Eren University

Ana Başlıklar:

Statistics (Other) | Statistical Data Science

Dergi:

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi Vol.14 Issue 3, pp.1403-1419

Belge Tipi:

Makale

Yayın Tarihi:

9/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001359241

pISSN:

2147-3129

Machine Learning and Socioeconomic Indicators

Data Analysis

Public Policies

Cognitive Outcomes of Education Policies

Planning with IQ Estimation

Sayı

Makale detayları

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

Yazarlar: Doruk Ayberkin | Özel Sebetci

Yayıncı: Bitlis Eren University

Ana Başlıklar: Statistics (Other) | Statistical Data Science

Dergi: Bitlis Eren Üniversitesi Fen Bilimleri Dergisi Vol.14 Issue 3, pp.1403-1419

Belge Tipi: Makale

Yayın Tarihi: 9/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001359241

pISSN: 2147-3129

Machine Learning and Socioeconomic Indicators

Data Analysis

Public Policies

Cognitive Outcomes of Education Policies

Planning with IQ Estimation

Sayı

Yazarlar:

Doruk Ayberkin | Özel Sebetci

Yayıncı:

Bitlis Eren University

Ana Başlıklar:

Statistics (Other) | Statistical Data Science

Dergi:

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi Vol.14 Issue 3, pp.1403-1419

Belge Tipi:

Makale

Yayın Tarihi:

9/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001359241

pISSN:

2147-3129