A Review on Classifiers Configuration for Medical Services Applications Using Machine Learning

Abstract

Machine learning techniques are commonly
used for prediction and classification tasks in
the field of medical diagnosis. The goal of
ML approaches is to classify diseases more
precisely and effectively for illness diagnosis.
The systems and equipment used in the lifecare of patients are advancing gradually. In
turn, this growth lengthens the average
human lifespan. However, these healthcare
systems face several difficulties and problems,
including misrepresenting patient
information, protecting data privacy, lacking
accurate data, lacking medical information,
using classifiers to make predictions, and
many other things. Numerous illness detection
and prediction systems, including expert
systems, clinical prediction systems, decision
support systems, and personal health record
systems have been created to address these
concerns. These systems are designed to aid
medical professionals in making an accurate
diagnosis of disease. Finding the disease's
symptoms more precisely is crucial as it
simplifies the treatment process. ML
classifiers are used to make predictions based
on patient data, which can help identify
potential diseases at an early stage. However,
these classifiers require large amounts of highquality data to function effectively. Therefore,
efforts are being made to improve data
collection methods and develop more accurate
prediction models that might be referred to as
disease diagnosis. It is simple to treat the
disease once the symptoms are accurately
detected.