Proceedings of the International Conference on Inventive Computing and Informatics (ICICI 2017) IEEE Xplore Compliant - Part Number: CFP17L34-ART, ISBN: 978-1-5386-4031-9
A Mixed Classification Approach for the Prediction of Parkinson’s disease using Nonlinear Feature Selection Technique based on the Voice Recording Satyabrata Aich1, Mangal Sain2, Jinse Park3, Ki-Won Choi4 and Hee-Cheol Kim4 1
Department of Computer Engineering, Inje University, S. Korea 2 Department of Computer Engineering, Dongseo University, 3 Department of Neurology, Inje Univesrity College of Medicine, S.Korea 4 Institute of Digital Anti-aging Healthcare, Inje University, South Korea S.Korea
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[email protected] Abstract— In recent years, the people affected with Parkinson’s disease (PD) are increasing with the increase in the old age population worldwide. PD affects 2-3% of the population over the age of 65 years. As the diseases progresses it produces different abnormalities in the spinal cords and brain cells that direct affect the gait, speech, and memory. Some of the recent works pointed out that artificial intelligence technique has been successfully applied to assess the disease at different stage using the gait features as well as speech related features. So in this paper an attempt has been made to `distinguish PD group from the healthy control group based on voice recordings with selected features and different classification techniques such as linear classifiers, nonlinear classifiers and Probabilistic classifiers. We have used recursive feature elimination algorithm (RFE) for selection of important features. We have implemented above mentioned classification technique and found an accuracy of 97.37%, and sensitivity of 100% with linear classifier (SVM) compared with the other classifier. We have also compare the other performance metrics such as sensitivity, specificity, positive predictive value, and negative predictive by implementing the classification techniques. This analysis helps the medical practitioner to distinguish PD from healthy group by using voice recordings. Keywords— Parkinson’s disease, classifiers, feature selection, voice recording, performance metrics
I. INTRODUCTION In recent years with the increase rate of old age population, Parkinson’s disease (PD) getting lot of attention because it is the 2nd most common neurological disease among the old age people throughout the world. With regard to economic perspective as well as social perspective of any country it became a challenging problem for biomedical engineers to predict the Parkinson’s disease at the early stage. The Parkinson’s disease is belongs to the category of neurodegenerative disease in which lost of brain cells and spinal cord leading to a disturbance of movement, vocal impairment, postural instability [1, 2, 3]. As the disease progressed in case of PD, close to 90% of the PD patients
suffered with voice and speech disorders [4]. Since the PD is progressive in nature by the time it is diagnosed, it almost degenerated 60% of nigrostriatal neurons and depleted 80% of striatal dopamine [5]. Therefore it is necessary to detect the PD at early stage and diagnosed it before it makes any severe damage to the patients. To improve and maintain the quality of life of the patients it is necessary to do the early diagnosis [6]. In the past the diagnosis of the PD patients was done by observing and interviewing the patients using the Unified Parkinson’s Disease rating scale (UPDRS) [7]. However, the traditional diagnosis needs lot of observations related to the daily living activities, motor skills and other neurological parameters to assess the progression of PD, but this process is not suitable for the early detection of the PD. With respect to the past research it is found that artificial intelligence and machine learning techniques have good potential for the classification and it also found that the classification system helps to improve the accuracy and the reliability of the diagnosis and also minimize the errors as well as make the system more efficient [8]. Improvement on the prediction of accuracy on the progression of PD is getting lot of attention these days [9, 10]. So in this paper an attempt has been made to check the improvement in the accuracy while classifying the PD group from the healthy control group by using nonlinear feature selection algorithm and different classification methods for classification of diseases based on train and test datasets of the voice recordings. The structure of the paper is organized as follows: Section 2 presents the past work related to classification model used for voice datasets. Section 3 describes about the methodologies used for this research work. Section 4 describes about the result of feature selection as well as the result of classification. Section 5 describes about the conclusion and future work. II. RELATED WORK Das et al has done a comparison based on different classification method on speech signals for effective diagnosis of PD. He used four classification methods such as neural
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Proceedings of the International Conference on Inventive Computing and Informatics (ICICI 2017) IEEE Xplore Compliant - Part Number: CFP17L34-ART, ISBN: 978-1-5386-4031-9
networks, regression, for effective diagnosis of PD. He used four different classification methods such as Neural networks, Regression, DMneural, and Decision tree and found that neural network is the best among the four classifier with accuracy of 92.9%. [11] Bhattacharya and Bhatia used SVM based method with different kernel to distinguish the Parkinson group from the healthy group by using Weka data mining tool. They have analyzed the accuracy based on the variation of Receiver Operating Characteristics (ROC) [12]. Polat used fuzzy c-means clustering feature weighting and kNN classification technique for the detection of PD. They found the combined approach perform better in the classification of PD [13]. Eskidere et al have analyzed the performance of Least Square Support Vector Machines (LSSVM), Multilayer Perceptron Neural Network (MLPNN), and General Regression Neural Network (GRNN) regression methods to track remotely the progression of PD. They found LSSVM produces best result compared to the other three methods [14]. Li et al proposed a fuzzy based method transformation system to extract good features and then used Principal Component Analysis (PCA) to find the optimal features among them. They have used SVM for the prediction of PD [15]. Gharehchopogh et al used artificial neural network based for the diagnosis of PD. They have used Multi-Layer Perceptron (MLP) with back-propagation and RBF to differentiate Parkinson patients. They found MLP performed well with an accuracy of 93.22% [16]. Froelich et al presented the diagnosis of PD based on the characteristics features of a person’s voice. They have used decision tree based classification approach using the threshold value. They found classification accuracy of 90% [17]. Shahbakhi et al proposed a method for diagnosis of PD based on speech analysis by using genetic algorithm (GA) and support vector machine. They have found accuracy of 94.50%, 93.66% and 94.22% on the basis of 4, 7 and 9 optimized features [18]. The above past works motivated us to try a different approach. In this paper we have tried a different way of selecting feature by using nonlinear feature selection algorithm called Random ForestRecursive Feature Elimination (RF-RFE) algorithm for selecting the feature and applied all the classification approach such as linear, nonlinear and probabilistic approach to study the improvement in the accuracy. III. PROPOSED TECHNIQUE The flow chart of the proposed methodologies is shown in the figure 1.In this paper we have used the dataset created by Max little University Oxford, in collaboration with the National Centre for Voice and Speech, Denver, Colorado, who recorded the speech signals [19]. The original data collected from the dataset composed of voice measurements from 31 people out of which 23 were diagnosed with PD. We have used Random Forest-Recursive Feature Elimination (RF-RFE) algorithm on the original feature sets. The RFE selection method is basically a recursive process that ranks features according to some measure of their importance [20]. We have found 11 features after implementing the algorithm to the original feature sets. We have used nonlinear classifier with
decision tree for classification of groups are as follows RPART,C4.5,PART,Bagging classification and Regression tree(Bagging CART),Random Forest and Boosted C5.0 and probabilistic classifier as Naïve Bayes method, and linear classifier as SVM.
Figure 1. Flowchart of the proposed method
A. Performance Metrics The parameters used to compare the performance and validation of classifier are as follows: accuracy, sensitivity, specificity, positive predictive value (ppv), negative predictive value (npv). The sensitivity is defined as the ratio of true positives to the sum of true positives and false negatives. The specificity is defined as the ratio of true negatives to the sum of false positives and true negatives. In our research we have used the Positive predictive value and negative predictive value to check the present and absent of disease. So the ppv is the probability that the disease is present given a positive test result and npv is the probability that the disease is absent given a negative test result [21]. Accuracy is defined as the ratio of number of correct predictions made to the total
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Proceedings of the International Conference on Inventive Computing and Informatics (ICICI 2017) IEEE Xplore Compliant - Part Number: CFP17L34-ART, ISBN: 978-1-5386-4031-9
prediction made and the ratio is multiplied by 100 to make it in terms of percentage. IV. RESULT AND DISCUSSIONS We have used R programming language to write the code. We trained each classifier based on the trained data and predict the power of classifier on the test data. So each classifier able to show all the performance metrics based on the test data. A. Comparison of Accuracy
Figure 4. Specificity of different classifiers
Fig. 3 shows that SVM and NB have highest sensitivity among other classifiers. Both of the classifiers shows maximum sensitivity of 1 Fig. 4 shows that SVM has highest specificity among other classifiers. It shows maximum specificity of 0.9750. D. Comparison of PPV
Figure 2. Accuracy of different classifiers Figure 5. PPV of different classifiers Fig. 2 shows that SVM performs better in terms of accuracy among all the classifiers. It shows the Fig. 5 shows that C4.5 has highest PPV among other maximum accuracy of 97.37%. classifiers. It shows maximum PPV of 0.9810.
B. Comparison of Sensitivity
E. Comparison of NPV
Figure 3. Sensitivity of different classifiers
C. Comparison of Specificity
Figure 6. NPV of different classifiers
Fig. 6 shows that SVM and NB have highest NPV among other classifiers. It shows maximum NPV of 1. V. CONCLUSION AND FUTURE WORK In this paper we have used linear, nonlinear and probabilistic based classifier to classify the PD and control group and we found good result by achieving an accuracy of 97.37% using the linear classifier. The nonlinear feature selection algorithm based features able to provide good performance in most of the classifiers. We have implemented different classifiers such as Recursive partitioning decision
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Proceedings of the International Conference on Inventive Computing and Informatics (ICICI 2017) IEEE Xplore Compliant - Part Number: CFP17L34-ART, ISBN: 978-1-5386-4031-9
tree (RPART), C4.5, PART, Bagging classification and Regression tree (Bagging CART), Random Forest and Boosted C5.0, Naïve Bayes method, and SVM. We found SVM has performed well in terms accuracy, sensitivity, specificity and NPV among all the classifier. In the future we will try other feature reduction techniques and as well as other classification technique to compare the performance of all the parameters of the performance metrics. REFERENCES [1] [2]
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