Parkinson s Disease Diagnosis by k-nearest Neighbor Soft Computing Model using Voice Features

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1 Parkinson s Disease Diagnosis by k-nearest Neighbor Soft Computing Model using Voice Features Chandra Prakash Rathore 1, Rekh Ram Janghel 2 1 Department of Information Technology, Dr. C. V. Raman University, Kota, Bilaspur , Chhattisgarh, India, 2 Department of Information Technology, National Institute of Technology, Raipur , Chhattisgarh, India, Authors rathore_1st@yahoo.com 1, rrj.iiitm@gmail.com 2 Abstract: Parkinson's disease is a chronic and progressive disorder of the central nervous system primarily caused by dopamine loss. It is prevalent among elder population effecting approx. 6.3 million people worldwide. In current paper a soft computing model is implemented using k-nearest neighbor classifier to diagnose Parkinson's disease based on voice features and its performance is evaluated on performance measures, viz., true positive, false positive, false negative, true negative, accuracy, sensitivity, specificity, RMSE over training and testing datasets. Later, performance of the soft computing model is also evaluated on reduced feature vector data sets created by applying correlation and principal component analysis (PCA) methods. The soft computing model exhibited 79.74% accuracy and RMSE on full feature vector training data set; 76.38% accuracy and RMSE on correlation reduced 10-feature vector training dataset; 81.8% accuracy and RMSE on PCA reduced 10-feature vector training dataset. Keywords: KNN, Parkinson Disease Diagnosis, Pearson's correlation, Principal Component Analysis 1. INTRODUCTION Parkinson's disease is a chronic and progressive disorder of the central nervous system primarily caused by dopamine loss besides viruses (encephalitis inducers), exposure to pesticides, head shocks, free radicals, ingestion of drugs (heroine), fault of mitochondrial genes. Crude prevalence rate of the disease per one lakh people is 27 in south India, 14 in north India, 16 in east India, 41 in rural India, 14 in urban India, in France, in the United Kingdom, 780 in Australia, 352 in the United States of America, in China, in Spain [1-2]. PD is prevalent among elder population. It reduces quality of life of the patients and impose economic burden on them as well. There is no cure available for the disease presently, but severity of the disease can be reduced by diagnosing and taking proper treatment like medication, surgery, rehabilitation, palliative care, balanced diet etc. The disease occurs in quite different ways from one patient to another and symptoms become more apparent as the disease advances. PD patients generally exhibit various symptoms like bradykinesia, tremor at rest, dementia, change in voice, gait, facial expressions and handwriting pattern, stiffness in limbs and trunk, visual problems, reduced cognitive function, depression, anxiety, rigidity, postural instability, memory loss, confusion which manifest with thinking, skin problems, sleep problems, muscle cramps etc. Practitioners try to diagnose the disease using the disease symptoms, medical history and various clinical tests and measures of the daily living activities. Manual clinical evaluations are subjective in nature and different physicians can assign different scores to the same patient. Therefore, an expert system is required, which can diagnose the disease on quantitative measures. In current paper, a soft computing expert system is implemented using k-nearest neighbor (knn) classifier to diagnose the disease quantitatively using voice features [3]

2 2. LITERATURE REVIEW Literature review demonstrates that soft computing models have been instrumented in diagnosing various diseases i.e. lung disease, liver disease, heart disease, diabetes, breast cancer etc. and played vital role in diagnosing PD as well. Sakar et al. collected various types of voice samples those included sustained vowels, words, and sentences compiled from a set of speaking exercises performed by PD patients. They investigated Parkinson dataset using machinelearning tools and found that sustained vowels carry more information to discriminate PD patients from the healthy persons [4]. Vikas and Sharma investigated voice parameters like pitch, formants, jitters, shimmer, glottal pulse and mel-frequency cepstral coefficient for PD patients and healthy controls. They observed that pitch of male PD patients is higher and formants of PD patients have more variation than healthy controls. Glottal pulse of normal person shows similar pattern while PD patients do not. Jitter and shimmer values are more in PD patients than healthy controls [5]. Tsanas et al. proposed a system for rapid, remote replication of Unified Parkinson s Disease Rating Scale (UPDRS) assessment on Parkinson s patients using simple, self-administered and noninvasive speech tests. They extracted 16 clinically useful features from speech of PD patients and applied feature selection algorithm to get prominent feature set. They statistically mapped the selected subset of the features to UPDRS using linear and nonlinear regression techniques. The system showed clinically useful accuracy i.e. only about 7.5 UPDRS points difference from the clinicians estimates [6]. Yadav et al. developed decision stump i.e. tree classifier, logistic regression i.e. statistical classifier and sequential minimization optimization i.e. support vector machine (SVM) classifier models for diagnosis of Parkinson s disease using speech features extracted from PD patients and controlled subjects. Dataset used in the classifier models consisted of a range of biomedical voice measurements from 23 PD patients and 8 controlled subjects. 10-cross fold based verification showed that among these classifiers SVM classifier model performed very well with 76% accuracy, 97% sensitivity and statistical classifier model performed worst with 64% accuracy and 64% sensitivity [7]. Ji and Li proposed a SVM model for PD diagnosis with dysphonia measurements as input feature vector. In order to identify suitability of the dysphonia measurements for tele monitoring of the disease, a feature-ranking method and its ensemble version based on improved energy learning model were used. Experimental results demonstrated that the ensemble version provided better performance than other classical methods i.e. simba, relief and local learning-based feature selection methods [8]. Bocklet et al. employed acoustic features, prosodic features and features derived from a two-mass model of the vocal folds on different kinds of speech tests like sustained phonations, syllable repetitions, read texts and monologues to classify PD patients and controlled subjects using SVM classifier. The system exhibited 91%, 88%, 79% recognition efficiency in classification of PD patients from controlled subjects with prosodic modeling, acoustic modeling and vocal modeling respectively. They also showed that read texts and monologues are the most meaningful texts when it comes to the automatic detection of PD based on articulation, voice and prosodic evaluations [9]. Xiao proposed a method for PD diagnosis by genetic algorithm and SVM using acoustic characteristics of PD patients for improving the diagnosis accuracy [10]. Rathore et al. implemented adaptive boosting and classification tree classifier based soft computing models to diagnose the disease using voice features where adaptive boosting classifier outperformed other classifier on PCA selected 15-feature vector testing dataset with 67.00% accuracy, 67.35% sensitivity, 66.67% specificity and RMSE [11]. Caesarendra et al. presented a pattern recognition method using voice features for multi-class classification of the disease based on PCA, LDA and SVM. PCA and LDA were used for reducing feature vector and SVM was used for classification. Results demonstrated that PCA features provided better accuracy than LDA selected features and single features [12]

3 3. EXPERIMENT AND RESULTS The experiment follows a series of activities i.e. voice dataset collection, data cleaning, data normalization, feature vector reduction, soft computing modeling, training and testing, and result analysis. The voice dataset was collected from UCI Machine Learning Repository [13] which was provided by Sakar et al. [4]. The dataset consists of 26-voice features (specified in Table-1) taken from 20 PD patients and 20 healthy controls. The dataset was cleaned by box plot outlier detection and removal method. The dataset was normalized by min-max normalization method and scaled between 0 and 1. For feature vector reduction two methods were employed i.e. Pearson's correlation method and PCA. According to [14] Pearson's correlation coefficient r is covariance of two variables divided by product of their standard deviations. For two datasets {x 1,,x i } and {y 1,,y i } containing n values r is defined as r = n i=1 (x i x ) (y i y ) n i=1 (x i x ) 2 n i=1 (y i y ) 2 Here, x andy are mean of the datasets {x 1,,x i } and {y 1,,y i } respectively defined below. x = 1 n y = 1 n n x i i=1 n y i i=1 PCA is a multivariate analysis technique used to reduce dimension of a data set by transforming it to a new set of variables called principal components, which are uncorrelated and ordered so that first few components can retain most of the variation present in all of the original variables. Assuming X is a data matrix, with column-wise zero empirical mean, having n rows and p columns. The transformation can be defined by a set of pdimensional vectors of weights w (k) = (w 1,, w p ) (k) that maps each row vector x (i) of X to a new vector of principal component scores t (i) = (t 1,, t m ) (i) given by t k(i) = x (i). w (k) for i = 1,, n; k = 1,, m in such a way that the individual variables of t considered over the data set successively inherit the maximum possible variance from x, with each loading vector w constrained to be a unit vector [15-16]. The soft computing model was implemented using knn classifier, which is an instance-based nonparametric learning method used for classification. In this method class label of a new sample of test data set is predicted by determining k nearest neighbors in feature space and then voting of the class labels they belong to. Euclidean distance is used as a measure to determine nearest neighbors. Euclidean distance between points (p, q) is defined as, d(p, q) = n i=1 (q i p i ) 2 The soft computing model was trained and tested on various types of voice training and testing datasets one by one, i.e. full feature vector voice dataset (Voice-26), 15-feature vector dataset where features reduced by correlation method (Corr-15), 10-feature vector dataset where features reduced by correlation method (Corr-10), 15- feature vector dataset where reduced feature vector obtained by PCA method (PCA-15), 10- feature vector dataset where reduced feature vector obtained by PCA method (PCA-10). Performance of the soft computing model was evaluated on performance measures true positive (TP), false positive (FP), false negative (FN), true negative (TN), accuracy, sensitivity, specificity, and root mean square error (RMSE). Confusion matrix for the soft computing model's performance on training and testing datasets are specified in Table 2 and Table 3 respectively. The soft computing model performed well on PCA-10 training and Voice-26 testing datasets. Performance matrix for the soft computing model's performance on training and testing datasets are specified in Table 4 and Table 5 respectively. The soft computing model demonstrated 79.74% accuracy and RMSE on Voice-26 training data set; 79.85% accuracy and RMSE on Corr-15 training dataset; 76.38% accuracy and RMSE on Corr-10 training dataset; 80.82%

4 accuracy and RMSE on PCA-15 training dataset; 81.8% accuracy and RMSE on PCA- 10 training dataset; 69.0% accuracy and RMSE on Voice-26 testing data set; 66.0% accuracy and RMSE on Corr-15 testing dataset; 59.0% accuracy and RMSE on Corr-10 testing dataset; 65.0% accuracy and RMSE on PCA-15 testing dataset; 59.0% accuracy and RMSE on PCA-10 testing dataset. In Figure 1-4 comparative study is performed of soft computing model's performance on training and testing datasets based on accuracy, sensitivity, specificity and RMSE respectively. The soft computing model exhibited better performance on PCA-10 training datasets with 81.8% accuracy, 83.44% sensitivity, 80.13% specificity, RMSE and on Voice-26 testing datasets with 69.00% accuracy, 83.67% sensitivity, 54.90% specificity, RMSE. Table 2: Confusion matrix for training datasets Data Set Total Records TP FP FN TN Voice Corr Corr PCA PCA Table 3: Confusion matrix for testing datasets Data Set Total Records TP FP FN TN Voice Corr Corr PCA PCA Table 1: Voice features used in experiment Feature No. Feature Name 1-5 Jitter (local), Jitter (local, absolute), Jitter (rap), Jitter (ppq5), Jitter (ddp) 6-11 Shimmer (local), Shimmer (local, db), Shimmer (apq3), Shimmer (apq5), Shimmer (apq11), Shimmer (dda) AC, NTH, HTN Median pitch, Mean pitch, Standard deviation, Minimum pitch, Maximum pitch Number of pulses, Number of periods, Mean period, Standard deviation of period Fraction of locally unvoiced frames, Number of voice breaks, Degree of voice breaks Table 4: Performance matrix for training datasets Data Set Accuracy Sensitivity Specificity (%) (%) (%) RMSE Voice Corr Corr PCA PCA Table 5: Performance matrix for testing datasets Accuracy Sensitivity Specificity Data Set RMSE (%) (%) (%) Voice Corr Corr PCA PCA

5 4. CONCLUSION Fig. 1: Comparison of accuracy of knn classifier on Fig. 2: Comparison of sensitivity of knn classifier on Parkinson's disease is prevalent among elder population effecting approx. 6.3 million people worldwide. In current paper a soft computing model is implemented using knn classifier based on voice features to diagnose the disease. The soft computing model is trained and tested on various kinds of voice training and testing datasets. Box plot method was used to clean the datasets and min-max normalization was employed to normalize the data. The soft computing models' performance was evaluated on performance measures viz. TP, FP, FP, TN, accuracy, sensitivity, specificity, and RMSE. The soft computing model exhibited better performance on PCA-10 training datasets with 81.8% accuracy, 83.44% sensitivity, 80.13% specificity, RMSE and on Voice-26 testing datasets with 69.00% accuracy, 83.67% sensitivity, 54.90% specificity, RMSE. Overall, soft computing model outperformed others on PCA-10 training dataset. It also takes less time to train and test soft computing model on reduced feature vector dataset compared to full feature vector dataset. In future the disease diagnosis efficiency can be increased and RMSE can be reduced by devising soft computing model ensemble. REFERENCES: [1] Balamurugan, N. & Vivekanandan, M. (2013). Parkinson s disease: medical management. Medicine Update, vol. 23, pp Fig. 3: Comparison of specificity of knn classifier on [2] Muthane, U. B., Ragothaman, M., & Gururaj, G. (2007). Epidemiology of Parkinson s disease and movement disorders in india: problems and possibilities. Journal of the Association of Physicians of India, vol. 55, pp [3] Shukla, A., Rathore, C. P., & Bhansali, N. (2016). Parkinson's disease detection with gait recognition using soft computing techniques. In Y. Morsi, A. Shukla, & C. Rathore (Eds.), Optimizing Assistive Technologies for Aging Populations, pp Hershey, PA: IGI Global. doi: / ch014 Fig. 4: Comparison of RMSE of knn classifier on [4] Sakar, B. E., Isenkul, M. E., Sakar, C. O., Sertbas, A., Gurgen, F., Delil, S., Apaydin, H., & Kursun, O

6 (2013). Collection and analysis of a Parkinson speech dataset with multiple types of sound recordings. IEEE Journal of Biomedical and Health Informatics, vol. 17(4), pp [5] Vikas & Sharma, R. K. (2014). Early detection of Parkinson s disease through voice. IEEE International Conference on Advances in Engineering and Technology. Nagapattinam, India. [6] Tsanas, A., Little, M. A., McSharry, P. E., & Ramig, L. O. (2010). Accurate telemonitoring of parkinson s disease progression by noninvasive speech tests. IEEE Transactions on Biomedical Engineering, vol. 57(4), pp [7] Yadav, G., Kumar, Y., & Sahoo, G. (2012). Predication of Parkinson s disease using data mining methods: a comparative analysis of tree, statistical and support vector machine classifiers. National Conference on Computing and Communication Systems. Durgapur, India. [8] Ji, W. & Li, Y. (2012). Energy-based feature ranking for assessing the dysphonia measurements in Parkinson detection. IET Signal Processing, vol. 6(4), pp [9] Bocklet, T., Noth, E., Stemmer, G., Ruzickova, H., & Rusz, J. (2011). Detection of persons with Parkinson s disease by acoustic, vocal, and prosodic analysis. IEEE Workshop on Automatic Speech Recognition and Understanding. Waikoloa, HI, USA. [10] Xiao, H. (2012). Diagnosis of Parkinson s disease using genetic algorithm and support vector machine with acoustic characteristics. International Conference on BioMedical Engineering and Informatics. Chongqing, China. [11] Rathore, C. P., Janghel, R. R., Verma, K., & Rathore, S. (2017). Parkinson s disease diagnosis by adaptive boosting and classification tree using voice features. CSVTU International Journal of Biotechnology, Bioinformatics and Biomedical, vol. 2(1), pp [12] Caesarendra, W., Ariyanto, M., Setiawan, J. D., Arozi, M., & Chang, C. R. (2014). A pattern recognition method for stage classification of Parkinson s disease utilizing voice features. IEEE Conference on Biomedical Engineering and Sciences. Kuala Lumpur, Malaysia. [13] Lichman, M. (2013). UCI Machine Learning Repository [ Irvine, CA: University of California, School of Information and Computer Science. [14]Pearson correlation coefficient, n_coefficient. Accessed on 01-Sep [15] Principal component analysis, nt_analysis. Accessed on 01-Sep [16] Jolliffe, I. T. (2002). Principal Component Analysis. Springer-Verlag, New Yark, USA