Millimeter-wave (mmWave) radar systems have emerged as a promising technology for object detection, particularly in security screening applications. This study explores the effectiveness of different radar data features, including range azimuth (RA), range Doppler (RD), azimuth elevation (AE), range azimuth elevation (RAE), range Doppler azimuth (RDA), and range Doppler azimuth elevation (RDAE), in improving object detection performance. Using data collected from Texas Instruments’ TIDEP 01212 cascaded mmWave radar system, the study investigates how these radar data features perform across several machine learning models, including LightGBM, random forest, support vector machines (SVM), and logistic regression. The dataset comprises radar scans of a person in both open carry and concealed scenarios, involving different objects such as a phone and scissors. The results show that SVM achieves perfect accuracy, precision, recall, and F1 scores when used with AE or RDAE data features.