Artificial Intelligence Model for Parkinson Disease Detection using Machine Learning Algorithms

Abstract
Background
In order for Parkinson's disease (PD) treatment and examination to be logical, a key requirement is that estimates of disease stage and severity are quantitative, reliable, and repeatable. The PD research in the past 50 years has been overwhelmed by the subjective emotional evaluation of human’s understanding of disease characteristics during clinical visits.
Method
The Parkinson's disease data set contains 23 features and 197 instances, of which 8 patients are sound and 23 patients, are analyzed as PD patients. Relying on chi2 test, extra trees classifier and correlation matrix as feature extraction strategies and relying on Decision Trees, K Nearest Neighbors, Random Forests, Bagging, AdaBoosting and Gradient Boosting as supervised AI calculations for permutation calculations. The calculation is based to obtain higher classifier accuracy, as well as ROC curves accuracy.
Results
Three conspicuous component selection strategies allow each of the 23 features to select 10 best performing features. The DT classifier has a higher accuracy of 94.87% in a dataset with 23 attributions, just like a dataset with 11 features. These results are also checked by ROC curve (AUC = 98.7%).
Conclusions
This calculation significantly separates PD patients from patients at the individual level, thus ensuring the use of computer-based findings in clinical practice.