Decision tree algorithms are widely used for solving classification and regression problems. Their popularity can be attributed to their transparent nature, simplicity, easy interpretability, faster classification speed, and strong decision rules. However, decision tree induction algorithms face various inherent and external limitations, such as overfitting, high sensitivity to noise and outliers, and instability with minimal data variations. In this study, we introduce an innovative approach to enhance traditional decision tree algorithms [e.g., Iterative Dichotomiser 3 (ID3), C4.5, and Classification and Regression Trees (CART)] by incorporating feature selection techniques. The proposed approach aims to enhance the accuracy and efficiency of decision tree models. Experiments were conducted on a real-world dataset of a hard disk drive (HDD) manufacturing process using the proposed approach. In comparison with a baseline where all features were utilized, the study highlighted a significant improvement in accuracy, indicating that the approach holds immense potential for optimizing decision tree algorithms and improving the HDD manufacturing process.