Over the years there has been an
increase in machine learning (ML) techniques, such as Random Forrest (RF),
Boosting (ADA), Logistic (GLM), Decision Trees (RPART), Support Vector Machines(SVM), and Artificial Neural Networks (ANN) applied to many medical fields. A
significant reason this has become the case is the capacity for human beings to
act as diagnostic tools over time. Stress, fatigue, inefficiencies, and lack of
knowledge all become barriers to high- quality outcomes.
There
have been studies regarding applications of data mining in different fields,
namely: biochemistry, genetics, oncology, neurology and However, literature
suggests that there are few comparisons of machine learning algorithms and
techniques in medical and biological areas. Of these ML algorithms, the most
common approach to develop nonparametric and nonlinear classifications is based
on ANNs.
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