Article: Transforming linear regression with AI-driven enhancements for superior forecasting robustness and interpretability Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.14 pp.44 - 61 Abstract: This is one of the first regression machines that data scientists will encounter because it is easier and easier to interpret. It suffers from complex, nonlinear and high dimensional data. In the context of finance, healthcare and climate domains, this study suggests a hybrid machine learning framework combining Adam, RMSProp, XGBoost, SVMs, and neural networks to significantly improve the regression performance. MSE, R2 and efficiency metrics are used to analyse real world datasets. The financial forecasting MSE is reduced by 18% and the healthcare R2 improved by 22%. Noisy data was easier to deal with for climate models. Because they preserved interpretability, features were indicated by SHAP values. Blending classical statistics with modern AI transforms the problem into more accurate, scalable, and interpretable models, providing robust solutions for todays complex data challenges, which are proven. Inderscience Publishers - linking academia, business and industry through research

Title: Transforming linear regression with AI-driven enhancements for superior forecasting robustness and interpretability

Authors: Ruili Zhang; Yifei Guo; Quanzhong Yang

Addresses: Digital Technology School, Sias University, Zhengzhou 450000, Henan, China ' College of Telecommunications and Smart Manufacturing, Sias University, Xinzheng 450000, Henan, China ' College of Medical Technology, Luoyang Polytechnic, Luoyang 471000, Henan, China

Abstract: This is one of the first regression machines that data scientists will encounter because it is easier and easier to interpret. It suffers from complex, nonlinear and high dimensional data. In the context of finance, healthcare and climate domains, this study suggests a hybrid machine learning framework combining Adam, RMSProp, XGBoost, SVMs, and neural networks to significantly improve the regression performance. MSE, R² and efficiency metrics are used to analyse real world datasets. The financial forecasting MSE is reduced by 18% and the healthcare R² improved by 22%. Noisy data was easier to deal with for climate models. Because they preserved interpretability, features were indicated by SHAP values. Blending classical statistics with modern AI transforms the problem into more accurate, scalable, and interpretable models, providing robust solutions for todays complex data challenges, which are proven.

Keywords: AI-driven regression enhancement; ensemble learning regression; predictive modelling AI; SHAP interpretability in regression; gradient optimisation regression; and automated feature selection in regression.

DOI: 10.1504/IJICT.2025.146368

International Journal of Information and Communication Technology, 2025 Vol.26 No.14, pp.44 - 61

Received: 25 Feb 2025
Accepted: 19 Mar 2025
Published online: 27 May 2025 *

Title: Transforming linear regression with AI-driven enhancements for superior forecasting robustness and interpretability

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