An issue with cardiac ventilation can result in death at any moment throughout a person's life. The apnea-hypopnea index (AHI) has historically been influenced by medical ventilation on heart failure; nevertheless, the sleep snore analysis is the best model to diagnose. The problems with ventilation are caused by problems with air pressure and blood circulation in the heart valves, where the pathological measures are continually detecting ventilation issues. Understanding the pathophysiology of OSA will have a direct impact on clinical treatment choices as well as the design of clinical studies. Treatments could be tailored to each patient's unique needs based on the fundamental reason to their OSA. Through the OSA treatment, patients could feel better, and understanding OSA symptoms and also outcomes will improve patient's health; as a result, the study reveals that most of the population are likely to benefit from specific OSA treatment approaches. For achieving the benefits of OSA treatment the classification accuracy is needed to be improved. So, in this research work, an LeNet-100 CNN-based deep learning technology is used to get information and apply the classification approaches. We obtained the heart failure dataset from the Kaggle website for conducting a meta-analysis. An accuracy of 93.25%, sensitivity of 97.29%, recall of 96.34%, and F measure of 95.34% had been attained. This approach outperforms the technology and is comparable to the present heart failure meta-analysis..