Objective: Dyspnea, also known as the patient's feeling of difficult or labored breathing, is one of the most common symptoms for respiratory disorders. Dyspnea is usually self-reported by patients using, for example, the Borg scale from 0 – 10, which is however subjective and problematic for those who refuse to cooperate or cannot communicate. The objective of this paper was to develop a learning-based model that can evaluate the correlation between the self-report Borg score and the respiratory metrics for dyspnea induced by exertion and increased airway resistance. Methods: A non-invasive wearable radio-frequency sensor by near-field coherent sensing was employed to retrieve continuous respiratory data with user comfort and convenience. Self-report dyspnea scores and respiratory features were collected on 32 healthy participants going through various physical and breathing exercises. A machine learning model based on the decision tree and random forest then produced an objective dyspnea score. Results: For unseen data as well as unseen participants, the objective dyspnea score can be in reasonable agreement with the self-report score, and the importance factor of each respiratory metrics can be assessed. Conclusion: An objective dyspnea score can potentially complement or substitute the self-report for physiologically induced dyspnea. Significance: The method can potentially formulate a baseline for clinical dyspnea assessment and help caregivers track dyspnea continuously, especially for patients who cannot report themselves.