Cardiovascular Disease Prediction using machine learning

Naresh Alapati, B. Prasad, Aditi Sharma, G. Kumari, Perumalla Jaya Bhargavi, Arikatla Alekhya, Botla Pravallika, K. Nandini
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引用次数: 0

Abstract

Big data helps to diagnose the medical healthcare conditions Early detection of health care conditions advantages to patient care. The best medical care is taken only when the disease is accurately known. If the medical data is inaccurate the data can’t be predicted in an efficient manner. So, to help for the best prediction of the disease we might use a machine learning algorithm that algorithms help us to diagnose the diseases. Using data sources from hospitalizations, As we have started a new model called convolution neural model it is related to multimodal disease forecasting method with the help of our understanding. In the present technology no study other than big data analytics has focused on two data types When we compare with many other illness identifying algorithms, the algorithm we have had started has correctness of 94.8% along with that convergence speed and it is more powerful than CNN-unimodal sickness identifying algorithm.
使用机器学习进行心血管疾病预测
大数据有助于诊断医疗健康状况早期发现健康状况有利于患者护理。只有在准确了解疾病的情况下,才能采取最好的医疗措施。如果医疗数据不准确,就不能有效地预测数据。所以,为了帮助最好地预测疾病,我们可以使用机器学习算法,算法帮助我们诊断疾病。利用医院的数据来源,我们建立了一个新的模型,称为卷积神经模型,它与我们对多模态疾病预测方法的理解有关。在目前的技术中,除了大数据分析之外,没有研究集中在两种数据类型上。当我们与许多其他疾病识别算法进行比较时,我们已经开始的算法的准确率为94.8%,并且收敛速度快,比cnn -单峰疾病识别算法更强大。
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