||In recent years, deep learning has a far-reaching impact on the medical field, deep learning combined with medical imaging application technology, and the use of machine learning ability to quickly and accurately judge a large number of medical imaging data, to help doctors improve the correct rate of disease diagnosis, and then with relevant pathological information to predict and analyze the risk and probability of disease occurrence, global scholars continue to study how to use the ability of deep learning in the development of intelligent medical. |
Brain diseases are complex and difficult to deal with, one of the brain diseases is dementia, the most common of which is Alzheimer′s disease, accounting for about 50%-70% of the number of dementia patients, there is no proven and effective drug treatment, so the current research direction towards delaying the deterioration of the course of the disease. Stroke is a common neurocreatic disease in brain diseases, and the pathological state of stroke patients before admission affects the chances of survival after surgery, this paper uses the deep learning model to make inferences about different types of medical data to predict the deterioration of Alzheimer′s disease and the survival rate after stroke surgery. In this paper, the random forest of machine learning, Gradient Boosting Trees, SVM and deep learning are used to compare the effectiveness by predicting the deterioration results of Alzheimer′s disease, and the recipient distinguishes the data set for the patient′s pathological information and the air pollution information, in an attempt to find out the risk factors for the deterioration of Alzheimer′s disease. In the same model, modules were created to evaluate the patient′s physiological indices for the pre-hospital stroke evaluation of stroke patients to see if they had an impact on post-operative survival.
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