Obstructive sleep apnea is a disease that occurs in connection to pauses in respiration during sleep. Detection of the disease is achieved using a polysomnography device, which is the gold standard in diagnosis. Diagnosis is made by the steps of sleep staging and respiration scoring. Respiration scoring is performed with at least four signals. Technical knowledge is required for attaching the electrodes. Additionally, the electrodes are disturbing to an extent that will delay the patient's sleep. It is needed to have systems as alternatives for polysomnography devices that will bring a solution to these issues. This study proposes a new approach for the process of respiration scoring which is one of the diagnostic steps for the disease. A machine-learning-based apnea detection algorithm was developed for the process of respiration scoring. The study used Photoplethysmography (PPG) signal and Heart Rate Variability (HRV) that is derived from this signal. The PPG records obtained from the patient and control groups were cleaned out using a digital filter. Then, the HRV parameter was derived from the PPG signal. Later, 46 features were derived from the PPG signal and 40 features were derived from the HRV. The derived features were classified with reduced machine-learning techniques using the F-score feature-selection algorithm. The evaluation was made in a multifaceted manner. Besides, Principal Component Analysis was performed to reduce system input (features). According to the results, if a real-time embedded system is designed, the system can operate with 16 PPG feature 95%, four PPG feature 93.81% accuracy rate. These success rates are highly sufficient for the system to work. Considering all these values, it is possible to realize a practical respiration scoring system. With this study, it was agreed upon that PPG signal may be used in the diagnosis of this disease by processing it with machine learning and signal processing techniques.