Asthma patients are prone to sudden respiratory failure, thus requiring a non-invasive and continuous monitoring method. This study investigates the use of Frequency-Modulated Continuous Wave (FMCW) radar to detect asthma by recording chest displacement signals without physical contact. The extracted respiratory features, including statistical descriptors and Mel-Frequency Cepstral Coefficients (MFCC), are analyzed using a machine learning classifier. To address class imbalance in the dataset, imbalanced learning strategies such as Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN) are applied. Among the tested models, the LightGBM-ADASYN combination achieved the best performance with an F1 score of 92.95% on the training data and 92.94% on the testing data. These results demonstrate the effectiveness of integrating FMCW radar with imbalanced learning approaches for the early detection of asthma, enabling timely intervention and improving patient safety.