Page 218 - Handbook of Deep Learning in Biomedical Engineering Techniques and Applications

208 Chapter 7 Early detection and diagnosis using deep learning

In the coming future, AI will also propose significant provision
for launching a combined therapeutic provision arrangement.
A trained and effectual combined medicinal facility structure
can be constructed with the assistance of info-grounded
classifications.

3. Early detection of diseases using deep
learning

Disease assessment by applying data mining and ML tech-
niques using patient health history and health data has been an
ongoing struggle for decades. The recent success of DL in the
various fields of ML has led to the switch to ML models that
can learn exposure and representations of raw data with less pre-
processing and produce more accurate results, making us not
only capable of predicting these diseases but also predicting the
reoccurrence of these diseases.
With the development of ML, much attention has been paid to
the prediction of diseases from the perspective of intensive study.
The primary focus is to use ML in healthcare to enhance patient
care with better outcomes. ML has made it easier to detect
different ailments and diagnose them correctly. Predictive anal-
ysis can accurately diagnose diseases with the help of various
effective ML algorithms and thus help treat patients.
The data generated by the healthcare industry are not always
used in its entirety, and its importance is often underestimated.
By using this large amount of data, the disease can be diagnosed,
predicted, or even treated. Diseases such as heart diseases,
cancer, tumors, and AD pose as one of the biggest threats to all
humankind, which can be detected using DL before it is too
late. This information, hidden in healthcare data, is then used
to make decisions for patient health. Furthermore, there is a
need for improvement in this area using data on healthcare.
Medical centers need to move forward to make better patient
diagnosis decisions and treatment options.
Computer literacy of healthcare helps people process small
and complex medical information and process it in clinical
supervision. This can then be further used by medical profes-
sionals in providing healthcare. Therefore, ML can increase
patient satisfaction when applied in healthcare.