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A Comparative Analysis of Machine Learning classifiers for Dysphonia-based classification of Parkinson’s Disease

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Abstract

Parkinson’s Disease is the second most common neurogenerative disease that affects the nervous system. There is no permanent cure for this disease, so, its early diagnosis is important to improve the quality of living of Parkinson patients. The distortion of the voice is one of the first symptoms to appear in Parkinson patients. Therefore, comparison and classification plays an important role. In this paper, a comparison of various classification techniques is done to show the potential of each classifier. The various classification techniques include SVM (Linear, RBF, Polynomial), DT, RF, LR, KNN, NB, MLP, AdaBoost, and XGBoost. Three different types of feature selection techniques are also explored to reduce the dimensionality of the dataset without affecting the accuracy much. The three different feature selection techniques include mRMR, GA, and PCA. The potential of voice features in classification process is also shown.

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  1. Computer Science and Engineering Department, Punjab Engineering College (Deemed to be University), Chandigarh, India

    Jinee Goyal, Padmavati Khandnor & Trilok Chand Aseri

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  1. Jinee Goyal
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Appendix A: Parameter selection for GA

Appendix A: Parameter selection for GA

Genetic Algorithm (GA) has four different input parameters which affects the performance of the system. These parameters include the number of generations, population size, crossover probability, and mutation probability. There is no direct formula to estimate the optimal size of these parameters. So, an experimental research design methodology is followed to tune these four input parameters.

1.1 Generation size

To select the optimal generation size, experiment has been done on 10, 20, 30, 40, 50 and 60 generations on the best performing classifier, i.e. XGBoost. The results are shown in Table 4

Table 4 Generation size
Full size table

The maximum accuracy is 90.60% for dataset 1 with 20 generations and maximum accuracy is 77.23% for dataset 2 with 40 generations. So, to choose from 20 or 40 generations, other performance parameters are checked and the generation size is chosen as 20. Specificity with generation 20 is greater as compared to specificity with generation size 40. The computation time with 20 generations is also reduced as compared to computation time with 40 generations.

1.2 Population size

The experiment for population size is done with size as 10, 15, 20, 25, and 30. The results are calculated with classifier having maximum accuracy, i.e. XGBoost and optimal generation size, i.e. 20. The results are shown in Table 5.

Table 5 Population size
Full size table

It can be observed from the table that there is an increasing trend with an increase in population size. As the population size increases, accuracy also increases. Increasing the population size does not increase much computation time. We have experimented the population size till 30 but it can be extended depending upon the requirement of the system, i.e. whether accuracy is a major concern or a trade-off is required between accuracy and computation time.

1.3 Crossover probability

The experiments of crossover probability are done with a probability of 0.5, 0.7, and 1 with XGBoost classifier as it is having maximum performance. The optimal generation size of 20 and a population size of 30 is taken for experimentation. The results are discussed in Table 6.

Table 6 Crossover probability
Full size table

It can be observed from the table that the crossover probability of 1 generates maximum accuracy. This means new generation children need to be crossover in every generation.

1.4 Mutation probability

The mutation probability is selected by experimenting with a probability of 0.1, 0.2, and 0.3. The experiments are done with best classifier (XGBoost), optimal generation size (20), optimal population size (30), and optimal crossover probability (1). The results are shown in Table 7.

Table 7 Mutation probability
Full size table

It can be observed from the table that optimal mutation probability is 0.2 as it generates maximum accuracy of 91.40% with dataset 1 and 77.31% with dataset 2.

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Goyal, J., Khandnor, P. & Aseri, T.C. A Comparative Analysis of Machine Learning classifiers for Dysphonia-based classification of Parkinson’s Disease. Int J Data Sci Anal 11, 69–83 (2021). https://doi.org/10.1007/s41060-020-00234-0

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