Abstract

The use of large datasets for predictive modeling in intelligent databases is frequently hindered by structural deficiencies, most notably missing records. Conventional imputation methods, such as mean or median substitution, distort statistical variance and fail to capture multivariate relationships among features. This paper presents a dynamic, AI-based data quality management framework that autonomously identifies and corrects structural anomalies prior to predictive modeling. Utilizing Classification and Regression Trees (CART), the system intelligently imputes missing categorical and numerical values by learning localized patterns from the observed portion of the dataset. To validate the integrity of the repaired database, the framework was evaluated on a credit risk dataset using a Tri-Ensemble of gradient boosting algorithms (XGBoost, LightGBM, and CatBoost), achieving an Area Under the ROC Curve (AUC) score of 0.9393. These results demonstrate that predictive, tree-based data imputation preserves the statistical distribution of the original data and substantially enhances the accuracy of downstream machine learning tasks. The proposed framework offers a scalable, automated solution for data preprocessing in distributed and intelligent database environments.

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