This study aims to compare decision tree and Analysis of Variance (ANOVA) techniques as feature selection methods, combined with Wavelet Packet Decomposition (WPD) for feature extraction, to enhance the diagnosis of faults in belt conveyor idlers. Belt conveyors are widely used in mining for efficient transport, but idlers composed of rollers are frequently subject to failure, making continuous monitoring essential to ensure reliability. Automated diagnostic solutions using vibration signals and machine learning rely on signal processing for feature extraction, often requiring dimensionality reduction or feature selection to improve classification accuracy. Due to the limitations of traditional techniques such as Principal Component Analysis (PCA) in handling temporal variations, Decision Tree and ANOVA emerge as effective alternatives for feature selection. This framework applied to each feature selection method, and Support Vector Machine (SVM) was used as a classification technique. The diagnostic performance of each method, including the case without feature selection, was evaluated. The results showed a higher diagnostic accuracy performance for the approaches that applied the features from the decision tree and from ANOVA. The improvement in the diagnosis of roller failures with feature selection was corroborated with the hit rates of failure mode, severity level, and location of a defective roller above 93.5%.