  Accurate and early prediction of a disease allows to plan and improve a
patient's quality of future life. During pandemic situations, the medical
decision becomes a speed challenge in which physicians have to act fast to
diagnose and predict the risk of the severity of the disease, moreover this is
also of high priority for neurodegenerative diseases like Parkinson's disease.
Machine Learning (ML) models with Features Selection (FS) techniques can be
applied to help physicians to quickly diagnose a disease. FS optimally subset
features that improve a model performance and help reduce the number of needed
tests for a patient and hence speeding up the diagnosis. This study shows the
result of three Feature Selection (FS) techniques pre-applied to a classifier
algorithm, Logistic Regression, on non-invasive test results data. The three FS
are Analysis of Variance (ANOVA) as filter based method, Least Absolute
Shrinkage and Selection Operator (LASSO) as embedded method and Sequential
Feature Selection (SFS) as wrapper method. The outcome shows that FS technique
can help to build an efficient and effective classifier, hence improving the
performance of the classifier while reducing the computation time.
