Comparing Support Vector Machine and Naïve Bayes Methods with A Selection of Fast Correlation Based Filter Features in Detecting Parkinson's Disease

Abstract

Dopamine levels fall due to brain nerve cell destruction, producing Parkinson's symptoms. Humans with this illness experience central nervous system damage, which lowers the quality of life. This disease is not deadly, but when people's quality of life decreases, they cannot perform daily activities as people do. Even in one case, this disease can cause death indirectly. Contrast support vector machines (SVM) and naive Bayesian approaches with and without fast correlation-based filter (FCBF) feature selection, this study attempts to determine the optimum model to detect Parkinson's disease categorization. In this study, datasets from the UCI Machine Learning Repository are used. The results showed that SVM with FCBF achieved the highest accuracy among all the models tested. SVM with FCBF provides an accuracy of 86.1538%, sensitivity of 93.8775%, and specificity of 62.5000%. Both methods, SVM and Naive Bayes, have improved in performance due to FCBF, with SVM showing a more significant increase in accuracy. This research contributed to helping paramedics determine if a patient has Parkinson's disease or not using characteristics obtained from data, such as movement, sound, or other pertinent factors.

Max Depth Impact on Heart Disease Classification: Decision Tree and Random Forest

“…A confusion matrix is a method for interpreting data, including both actual data and predictions from classification results. In classification, the goal is accurate categorization with minimal errors, and the confusion matrix aids in assessing the effectiveness of the categorization process [41]. To assess the model constructed with the confusion matrix by calculating the accuracy, recall, and precision values are shown in Formulas 6, 7 and 8 [42].…”