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Support Vector Machine (SVM) is an excellent Machine Learning (ML) method and Kernel Function (KF) in SVM can effectively accelerate the calculation and affect the results in ML. Therefore, the selection of KF has become a research hotspot. In small-sample medical detection learning tasks, there are often imbalanced data categories and data overlaps. To alleviate these situations, we adopt feature selection in the data preprocessing stage and optimize the support vector machine learner with fusion kernel function. In this paper, SVM based on Fusion KF(FKF-SVM) is used for Parkinson's Disease (PD) detection to improve the performance of traditional SVM. The improved KF used in the paper includes Analysis of Variance (ANOVA) kernel, triangular kernel, log kernel and wavelet kernel. The proposed KF is evaluated by using two popular and publicly available data sets from University of California Irvine (UCI) repository with different parameters like accuracy, recall, F1score, Area Under the Curve (AUC) in classification and Mean Square Error (MSE), Mean Absolute Error (MAE), R2score, Explained Variance Score (EVS) in regression. According to the experimental results, the triangular kernel is the winner in the classification that achieves an accuracy of 93%, recall of 96%, F1score of 96% and AUC of 97%, which is increased by ten percentage points at least compared to the traditional KF. In the regression, the ANOVA kernel is a superior KF which obtains an MAE of 2.3, R2score of 0.81 that is 200% lower than the traditional KF at least.
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