  It is of paramount importance to enhance medical practices, given how
important it is to protect human life. Medical therapy can be accelerated by
automating patient prediction using machine learning techniques. To double the
efficiency of classifiers, several preprocessing strategies must be adopted for
their crucial duty in this field. Feature selection (FS) is one tool that has
been used frequently to modify data and enhance classification outcomes by
lowering the dimensionality of datasets. Excluded features are those that have
a poor correlation coefficient with the label class, that is, they have no
meaningful correlation with classification and do not indicate where the
instance belongs. Along with the recurring features, which show a strong
association with the remainder of the features. Contrarily, the model being
produced during training is harmed, and the classifier is misled by their
presence. This causes overfitting and increases algorithm complexity and
processing time. These are used in exploration to allow solutions to be found
more thoroughly and in relation to a chosen solution than at random. TLSO,
PLSO, and LLSO stand for Tournament Logarithmic Snake Optimizer, Proportional
Logarithmic Snake Optimizer, and Linear Order Logarithmic Snake Optimizer,
respectively. A number of 22 reference medical datasets were used in
experiments. The findings indicate that, among 86 % of the datasets, TLSO
attained the best accuracy, and among 82 % of the datasets, the best feature
reduction. In terms of the standard deviation, the TLSO also attained
noteworthy reliability and stability. On the basis of running duration, it is,
nonetheless, quite effective.
