to facilitate users to quickly search for relevant
information. At the same time, access control is used
to prevent SQL injection attacks and reduce the
exposure of key information to users to ensure
database security. In terms of prediction, Bayesian
models are used to conduct correlation analysis on
various complications, and explainable artificial
intelligence is added to facilitate doctors to confirm
patients' symptoms faster. By calculating the
probability to find the main cause of the disease, the
accuracy and reliability of the prediction results are
ensured while excluding complications and other
erroneous data.
Although the Bayesian network assumes that each
condition is independent, the model is relatively
simple and computationally efficient and can avoid
erroneous data through probabilistic analysis.
However because of this assumption, the model
struggles to make complete predictions. Moreover,
this model requires a large number of training
samples for data training, and there is still a lack of
samples in this field, so it is still difficult for this
model to be accurately applied in disease diagnosis.
Due to the small number of relevant references, it
is difficult for this article to form an accurate
feasibility analysis conclusion. At the same time, due
to the lack of detailed understanding of the human
nervous system and convolutional neural networks, it
is still at a highly theoretical stage and is difficult to
apply in specific practice. However, the Bayesian
network proposed in this article can be used to
analyze new cases in the database, provide ideas for
subsequent research, and reduce the severe
psychological burden on patients due to the impact of
long-term complications and further aggravate
mental illness.
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