ABSTRACT

The leading cause of mortality is due to cardio vascular disease (CVD) globally. CVD is the major cause of death all over the world for the past years because an estimation of 17.5 million people died from CVD in 2012 and premature death from CVD is 37% below the age of 70. In health-care field, the data generated are large, critical, and more complex and multi-dimensional. In the current situation, the medical professionals working in the field of heart disease can predict up to 67% accuracy but the doctors need an accurate prediction of heart disease. The ultimate goal of this study is to early prediction of CVD by enhancing both predictive analysis and probabilistic classification.

Deep learning techniques such as CNN and RNN emulate human cognition and learn from training examples to predict future events. As a result, the future prediction of the cardiovascular disease has been found. The prediction of CVD can be used for the prevention of COVID-19 disease using deep learning algorithm. So, this can be employed to detect the early stage of the disease. The importance of the CVD refers to the conditions like narrowed or blocked blood vessels which may lead to some other diseases like heart attack, chest pain or stroke.

摘要

全球死亡的主要原因是心血管疾病（CVD）。CVD 是过去几年全世界死亡的主要原因，估计 2012 年有 1,750 万人死于 CVD，其中 37% 的人在 70 岁以下因 CVD 过早死亡。在医疗保健领域，生成的数据规模大、关键、更复杂、多维。在目前的情况下，从事心脏病领域工作的医疗专业人员可以预测高达67%的准确率，但医生需要对心脏病进行准确的预测。本研究的最终目标是通过增强预测分析和概率分类来早期预测 CVD。

CNN 和 RNN 等深度学习技术模拟人类认知，并从训练样本中学习来预测未来事件。由此，发现了心血管疾病的未来预测。使用深度学习算法预测 CVD 可用于预防 COVID-19 疾病。因此，这可以用来检测疾病的早期阶段。CVD 的重要性是指血管狭窄或阻塞等情况，可能会导致心脏病、胸痛或中风等其他疾病。