×

An intelligent Parkinson’s disease diagnostic system based on a chaotic bacterial foraging optimization enhanced fuzzy KNN approach. (English) Zbl 1411.92131

Summary: Parkinson’s disease (PD) is a common neurodegenerative disease, which has attracted more and more attention. Many artificial intelligence methods have been used for the diagnosis of PD. In this study, an enhanced fuzzy \(k\)-nearest neighbor (FKNN) method for the early detection of PD based upon vocal measurements was developed. The proposed method, an evolutionary instance-based learning approach termed CBFO-FKNN, was developed by coupling the chaotic bacterial foraging optimization with Gauss mutation (CBFO) approach with FKNN. The integration of the CBFO technique efficiently resolved the parameter tuning issues of the FKNN. The effectiveness of the proposed CBFO-FKNN was rigorously compared to those of the PD datasets in terms of classification accuracy, sensitivity, specificity, and AUC (area under the receiver operating characteristic curve). The simulation results indicated the proposed approach outperformed the other five FKNN models based on BFO, particle swarm optimization, genetic algorithms, fruit fly optimization, and firefly algorithm, as well as three advanced machine learning methods including support vector machine (SVM), SVM with local learning-based feature selection, and kernel extreme learning machine in a 10-fold cross-validation scheme. The method presented in this paper has a very good prospect, which will bring great convenience to the clinicians to make a better decision in the clinical diagnosis.

MSC:

92C50 Medical applications (general)
92C20 Neural biology
68T05 Learning and adaptive systems in artificial intelligence
62P10 Applications of statistics to biology and medical sciences; meta analysis
92-04 Software, source code, etc. for problems pertaining to biology

Software:

LIBSVM; GEMS
PDFBibTeX XMLCite
Full Text: DOI

References:

[1] de Lau, L. M.; Breteler, M. M., Epidemiology of Parkinson’s disease, The Lancet Neurology, 5, 6, 525-535 (2006) · doi:10.1016/S1474-4422(06)70471-9
[2] van den Eeden, S. K.; Tanner, C. M.; Bernstein, A. L.; Fross, R. D.; Leimpeter, A.; Bloch, D. A.; Nelson, L. M., Incidence of Parkinson’s disease: variation by age, gender, and race/ethnicity, American Journal of Epidemiology, 157, 11, 1015-1022 (2003) · doi:10.1093/aje/kwg068
[3] Dorsey, E. R.; Constantinescu, R.; Thompson, J. P.; Biglan, K. M.; Holloway, R. G.; Kieburtz, K.; Marshall, F. J.; Ravina, B. M.; Schifitto, G.; Siderowf, A.; Tanner, C. M., Projected number of people with Parkinson disease in the most populous nations, 2005 through 2030, Neurology, 68, 5, 384-386 (2007) · doi:10.1212/01.wnl.0000247740.47667.03
[4] Singh, N.; Pillay, V.; Choonara, Y. E., Advances in the treatment of Parkinson’s disease, Progress in Neurobiology, 81, 1, 29-44 (2007) · doi:10.1016/j.pneurobio.2006.11.009
[5] Harel, B. T.; Cannizzaro, M. S.; Cohen, H.; Reilly, N.; Snyder, P. J., Acoustic characteristics of Parkinsonian speech: A potential biomarker of early disease progression and treatment, Journal of Neurolinguistics, 17, 6, 439-453 (2004) · doi:10.1016/j.jneuroling.2004.06.001
[6] Rusz, J.; Cmejla, R.; Ruzickova, H.; Ruzicka, E., Quantitative acoustic measurements for characterization of speech and voice disorders in early untreated Parkinson’s disease, The Journal of the Acoustical Society of America, 129, 1, 350-367 (2011) · doi:10.1121/1.3514381
[7] Jankovic, J., Parkinsons disease: clinical features and diagnosis, Journal of Neurology, Neurosurgery & Psychiatry, 79, 368-376 (2008) · doi:10.1136/jnnp.2007.131045
[8] Massano, J.; Bhatia, K. P., Clinical approach to Parkinson’s disease: features, diagnosis, and principles of management, Cold Spring Harbor Perspectives in Medicine, 2, 6 (2012) · doi:10.1101/cshperspect.a008870
[9] Ho, A. K.; Iansek, R.; Marigliani, C.; Bradshaw, J. L.; Gates, S., Speech impairment in a large sample of patients with Parkinson’s disease, Behavioural Neurology, 11, 3, 131-137 (1998)
[10] Harel, B.; Cannizzaro, M.; Snyder, P. J., Variability in fundamental frequency during speech in prodromal and incipient Parkinson’s disease: A longitudinal case study, Brain and Cognition, 56, 1, 24-29 (2004) · doi:10.1016/j.bandc.2004.05.002
[11] Baken, R. J.; Orlikoff, R. F., Clinical Measurement of Speech and Voice (2000), San Diego, CA, USA: Singular Publishing Group, San Diego, CA, USA
[12] Brabenec, L.; Mekyska, J.; Galaz, Z.; Rektorova, I., Speech disorders in Parkinson’s disease: early diagnostics and effects of medication and brain stimulation, Journal of Neural Transmission, 124, 3, 303-334 (2017) · doi:10.1007/s00702-017-1676-0
[13] Little, M. A.; McSharry, P. E.; Hunter, E. J.; Spielman, J.; Ramig, L. O., Suitability of dysphonia measurements for telemonitoring of Parkinson’s disease, IEEE Transactions on Biomedical Engineering, 56, 4, 1015-1022 (2009) · doi:10.1109/TBME.2008.2005954
[14] Das, R., A comparison of multiple classification methods for diagnosis of Parkinson disease, Expert Systems with Applications, 37, 2, 1568-1572 (2010) · doi:10.1016/j.eswa.2009.06.040
[15] Åström, F.; Koker, R., A parallel neural network approach to prediction of Parkinson’s Disease, Expert Systems with Applications, 38, 10, 12470-12474 (2011) · doi:10.1016/j.eswa.2011.04.028
[16] Sakar, C. O.; Kursun, O., Telediagnosis of parkinson’s disease using measurements of dysphonia, Journal of Medical Systems, 34, 4, 591-599 (2010) · doi:10.1007/s10916-009-9272-y
[17] Li, D.-C.; Liu, C.-W.; Hu, S. C., A fuzzy-based data transformation for feature extraction to increase classification performance with small medical data sets, Artificial Intelligence in Medicine, 52, 1, 45-52 (2011) · doi:10.1016/j.artmed.2011.02.001
[18] Shahbaba, B.; Neal, R., Nonlinear models using Dirichlet process mixtures, Journal of Machine Learning Research, 10, 1829-1850 (2009) · Zbl 1235.62069
[19] Psorakis, I.; Damoulas, T.; Girolami, M. A., Multiclass relevance vector machines: sparsity and accuracy, IEEE Transactions on Neural Networks and Learning Systems, 21, 10, 1588-1598 (2010) · doi:10.1109/TNN.2010.2064787
[20] Guo, P. F.; Bhattacharya, P.; Kharma, N., Advances in Detecting Parkinsons Disease, Medical Biometrics, 306-314 (2010)
[21] Luukka, P., Feature selection using fuzzy entropy measures with similarity classifier, Expert Systems with Applications, 38, 4, 4600-4607 (2011) · doi:10.1016/j.eswa.2010.09.133
[22] Ozcift, A.; Gulten, A., Classifier ensemble construction with rotation forest to improve medical diagnosis performance of machine learning algorithms, Computer Methods and Programs in Biomedicine, 104, 3, 443-451 (2011) · doi:10.1016/j.cmpb.2011.03.018
[23] Spadoto, A. A.; Guido, R. C.; Carnevali, F. L.; Pagnin, A. F.; Falcão, A. X.; Papa, J. P., Improving Parkinson’s disease identification through evolutionary-based feature selection, Conference proceedings: IEEE Engineering in Medicine and Biology Society, 2011, 7857-7860 (2011)
[24] Polat, K., Classification of Parkinson’s disease using feature weighting method on the basis of fuzzy C-means clustering, International Journal of Systems Science, 43, 4, 597-609 (2012) · Zbl 1305.62375 · doi:10.1080/00207721.2011.581395
[25] Chen, H.-L.; Huang, C.-C.; Yu, X.-G., An efficient diagnosis system for detection of Parkinson’s disease using fuzzy \(k\)-nearest neighbor approach, Expert Systems with Applications, 40, 1, 263-271 (2013) · doi:10.1016/j.eswa.2012.07.014
[26] Zuo, W.-L.; Wang, Z.-Y.; Liu, T.; Chen, H.-L., Effective detection of Parkinson’s disease using an adaptive fuzzy \(k\)-nearest neighbor approach, Biomedical Signal Processing and Control, 8, 4, 364-373 (2013) · doi:10.1016/j.bspc.2013.02.006
[27] Sateesh Babu, G.; Suresh, S., Parkinson’s disease prediction using gene expression- A projection based learning meta-cognitive neural classifier approach, Expert Systems with Applications, 40, 5, 1519-1529 (2013) · doi:10.1016/j.eswa.2012.08.070
[28] Babu, G. S.; Suresh, S.; Mahanand, B. S., A novel PBL-McRBFN-RFE approach for identification of critical brain regions responsible for Parkinson’s disease, Expert Systems with Applications, 41, 2, 478-488 (2014) · doi:10.1016/j.eswa.2013.07.073
[29] Sateesh Babu, G.; Suresh, S.; Uma Sangumathi, K.; Kim, H. J., A Projection Based Learning Meta-cognitive RBF Network Classifier for Effective Diagnosis of Parkinson’s Disease, Advances in Neural Networks - ISNN 2012. Advances in Neural Networks - ISNN 2012, Lecture Notes in Computer Science, 7368, 611-620 (2012), Berlin, Germany: Springer, Berlin, Germany · doi:10.1007/978-3-642-31362-2_67
[30] Hariharan, M.; Polat, K.; Sindhu, R., A new hybrid intelligent system for accurate detection of Parkinson’s disease, Computer Methods and Programs in Biomedicine, 113, 3, 904-913 (2014) · doi:10.1016/j.cmpb.2014.01.004
[31] Gök, M., An ensemble of k-nearest neighbours algorithm for detection of Parkinson’s disease, International Journal of Systems Science, 46, 6, 1108-1112 (2015) · Zbl 1362.92029 · doi:10.1080/00207721.2013.809613
[32] Shen, L.; Chen, H.; Yu, Z.; Kang, W.; Zhang, B.; Li, H.; Yang, B.; Liu, D., Evolving support vector machines using fruit fly optimization for medical data classification, Knowledge-Based Systems, 96, 61-75 (2016) · doi:10.1016/j.knosys.2016.01.002
[33] Peker, M.; Şen, B.; Delen, D., Computer-aided diagnosis of Parkinson’s disease using complex-valued neural networks and mRMR feature selection algorithm, Journal of Healthcare Engineering, 6, 3, 281-302 (2015) · doi:10.1260/2040-2295.6.3.281
[34] Chen, H.-L.; Wang, G.; Ma, C.; Cai, Z.-N.; Liu, W.-B.; Wang, S.-J., An efficient hybrid kernel extreme learning machine approach for early diagnosis of Parkinson’s disease, Neurocomputing, 184, 4745, 131-144 (2016) · doi:10.1016/j.neucom.2015.07.138
[35] Cai, Z.; Gu, J.; Chen, H.-L., A New Hybrid Intelligent Framework for Predicting Parkinson’s Disease, IEEE Access, 5, 17188-17200 (2017) · doi:10.1109/ACCESS.2017.2741521
[36] Sakar, B. E.; Isenkul, M. E.; Sakar, C. O.; Sertbas, A.; Gurgen, F.; Delil, S.; Apaydin, H.; Kursun, O., Collection and analysis of a Parkinson speech dataset with multiple types of sound recordings, IEEE Journal of Biomedical and Health Informatics, 17, 4, 828-834 (2013) · doi:10.1109/jbhi.2013.2245674
[37] Zhang, H.; Yang, L.; Liu, Y.; Wang, P.; Yin, J.; Li, Y.; Qiu, M.; Zhu, X.; Yan, F., Classification of Parkinson’s disease utilizing multi-edit nearest-neighbor and ensemble learning algorithms with speech samples, Biomedical Engineering Online, 15, 1 (2016) · doi:10.1186/s12938-016-0242-6
[38] Abrol, V.; Sharma, P.; Sao, A. K., Greedy dictionary learning for kernel sparse representation based classifier, Pattern Recognition Letters, 78, 64-69 (2016) · doi:10.1016/j.patrec.2016.04.014
[39] Cantürk, İ.; Karabiber, F., A Machine Learning System for the Diagnosis of Parkinson’s Disease from Speech Signals and Its Application to Multiple Speech Signal Types, Arabian Journal for Science and Engineering, 41, 12, 5049-5059 (2016) · doi:10.1007/s13369-016-2206-3
[40] Jóźwik, A., A learning scheme for a fuzzy k-NN rule, Pattern Recognition Letters, 1, 5-6, 287-289 (1983) · doi:10.1016/0167-8655(83)90064-8
[41] Keller, J. M.; Gray, M. R.; Givens, J. A., A fuzzy K-nearest neighbor algorithm, IEEE Transactions on Systems, Man, and Cybernetics, SMC-15, 4, 580-585 (1985) · doi:10.1109/TSMC.1985.6313426
[42] Derrac, J.; García, S.; Herrera, F., Fuzzy nearest neighbor algorithms: Taxonomy, experimental analysis and prospects, Information Sciences, 260, 98-119 (2014) · doi:10.1016/j.ins.2013.10.038
[43] Liu, D.-Y.; Chen, H.-L.; Yang, B.; Lv, X.-E.; Li, L.-N.; Liu, J., Design of an enhanced Fuzzy k-nearest neighbor classifier based computer aided diagnostic system for thyroid disease, Journal of Medical Systems, 36, 5, 3243-3254 (2012) · doi:10.1007/s10916-011-9815-x
[44] Sim, J.; Kim, S.-Y.; Lee, J., Prediction of protein solvent accessibility using fuzzy k-nearest neighbor method, Bioinformatics, 21, 12, 2844-2849 (2005) · doi:10.1093/bioinformatics/bti423
[45] Huang, Y.; Li, Y., Prediction of protein subcellular locations using fuzzy k-NN method, Bioinformatics, 20, 1, 21-28 (2004) · doi:10.1093/bioinformatics/btg366
[46] Chen, H.-L.; Yang, B.; Wang, G.; Liu, J.; Xu, X.; Wang, S.-J.; Liu, D.-Y., A novel bankruptcy prediction model based on an adaptive fuzzy k-nearest neighbor method, Knowledge-Based Systems, 24, 8, 1348-1359 (2011) · doi:10.1016/j.knosys.2011.06.008
[47] Cheng, M.-Y.; Hoang, N.-D., A Swarm-Optimized Fuzzy Instance-based Learning approach for predicting slope collapses in mountain roads, Knowledge-Based Systems, 76, 256-263 (2015) · doi:10.1016/j.knosys.2014.12.022
[48] Cheng, M.-Y.; Hoang, N.-D., Groutability estimation of grouting processes with microfine cements using an evolutionary instance-based learning approach, Journal of Computing in Civil Engineering, 28, 4 (2014) · doi:10.1061/(asce)cp.1943-5487.0000370
[49] Passino, K. M., Biomimicry of bacterial foraging for distributed optimization and control, IEEE Control Systems Magazine, 22, 3, 52-67 (2002) · doi:10.1109/MCS.2002.1004010
[50] Liu, L.; Shan, L.; Dai, Y.; Liu, C.; Qi, Z., A Modified Quantum Bacterial Foraging Algorithm for Parameters Identification of Fractional-Order System, IEEE Access, 6, 6610-6619 (2018) · doi:10.1109/ACCESS.2018.2791976
[51] Hernandez-Ocana, B.; Chavez-Bosquez, O.; Hernandez-Torruco, J.; Canul-Reich, J.; Pozos-Parra, P., Bacterial Foraging Optimization Algorithm for Menu Planning, IEEE Access, 6, 8619-8629 (2018) · doi:10.1109/ACCESS.2018.2794198
[52] Othman, A. M.; Gabbar, H. A., Enhanced microgrid dynamic performance using a modulated power filter based on enhanced bacterial foraging optimization, Energies, 10, 6 (2017)
[53] Chouhan, S. S.; Kaul, A.; Singh, U. P.; Jain, S., Bacterial Foraging Optimization Based Radial Basis Function Neural Network (BRBFNN) for Identification and Classification of Plant Leaf Diseases: An Automatic Approach Towards Plant Pathology, IEEE Access, 6, 8852-8863 (2018) · doi:10.1109/ACCESS.2018.2800685
[54] Turanoğlu, B.; Akkaya, G., A new hybrid heuristic algorithm based on bacterial foraging optimization for the dynamic facility layout problem, Expert Systems with Applications, 98, 93-104 (2018) · doi:10.1016/j.eswa.2018.01.011
[55] Lv, X.; Chen, H.; Zhang, Q.; Li, X.; Huang, H.; Wang, G., An improved bacterial-foraging optimization-based machine learning framework for predicting the severity of somatization disorder, Algorithms, 11, 2, article 17 (2018) · doi:10.3390/a11020017
[56] Majhi, R.; Panda, G.; Majhi, B.; Sahoo, G., Efficient prediction of stock market indices using adaptive bacterial foraging optimization (ABFO) and BFO based techniques, Expert Systems with Applications, 36, 6, 10097-10104 (2009) · doi:10.1016/j.eswa.2009.01.012
[57] Dasgupta, S.; Das, S.; Biswas, A.; Abraham, A., Automatic circle detection on digital images with an adaptive bacterial foraging algorithm, Soft Computing, 14, 11, 1151-1164 (2010) · doi:10.1007/s00500-009-0508-z
[58] Mishra, S., A hybrid least square-fuzzy bacterial foraging strategy for harmonic estimation, IEEE Transactions on Evolutionary Computation, 9, 1, 61-73 (2005) · doi:10.1109/TEVC.2004.840144
[59] Mishra, S.; Bhende, C. N., Bacterial foraging technique-based optimized active power filter for load compensation, IEEE Transactions on Power Delivery, 22, 1, 457-465 (2007) · doi:10.1109/TPWRD.2006.876651
[60] Ulagammai, M.; Venkatesh, P.; Kannan, P. S.; Prasad Padhy, N., Application of bacterial foraging technique trained artificial and wavelet neural networks in load forecasting, Neurocomputing, 70, 16-18, 2659-2667 (2007) · doi:10.1016/j.neucom.2006.05.020
[61] Wu, Q.; Mao, J. F.; Wei, C. F.; Fu, S.; Law, R.; Ding, L.; Yu, B. T.; Jia, B.; Yang, C. H., Hybrid BF-PSO and fuzzy support vector machine for diagnosis of fatigue status using EMG signal features, Neurocomputing, 173, 483-500 (2016) · doi:10.1016/j.neucom.2015.06.002
[62] Yang, C.; Ji, J.; Liu, J.; Liu, J.; Yin, B., Structural learning of Bayesian networks by bacterial foraging optimization, International Journal of Approximate Reasoning, 69, 147-167 (2016) · Zbl 1344.68194 · doi:10.1016/j.ijar.2015.11.003
[63] Sivarani, T. S.; Joseph Jawhar, S.; Agees Kumar, C.; Prem Kumar, K., Novel bacterial foraging-based ANFIS for speed control of matrix converter-fed industrial BLDC motors operated under low speed and high torque, Neural Computing and Applications, 1-24 (2016)
[64] Zhao, F.; Liu, Y.; Shao, Z.; Jiang, X.; Zhang, C.; Wang, J., A chaotic local search based bacterial foraging algorithm and its application to a permutation flow-shop scheduling problem, International Journal of Computer Integrated Manufacturing, 29, 9, 962-981 (2016) · doi:10.1080/0951192x.2015.1130240
[65] Sun, Y.; Todorovic, S.; Goodison, S., Local-learning-based feature selection for high-dimensional data analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence, 32, 9, 1610-1626 (2010) · doi:10.1109/TPAMI.2009.190
[66] Chen, H.; Lu, J.; Li, Q.; Lou, C.; Pan, D.; Yu, Z.; Tan, Y.; Shi, Y.; Buarque, F., A New Evolutionary Fuzzy Instance-Based Learning Approach: Application for Detection of Parkinson’s Disease, Advances in Swarm and Computational Intelligence. Advances in Swarm and Computational Intelligence, Lecture Notes in Computer Science, 9141, 42-50 (2015), Springer International Publishing · doi:10.1007/978-3-319-20472-7_5
[67] Kapitaniak, T., Continuous control and synchronization in chaotic systems, Chaos, Solitons & Fractals, 6, C, 237-244 (1995) · Zbl 0976.93504 · doi:10.1016/0960-0779(95)80030-K
[68] Bäck, T.; Schwefel, H.-P., An overview of evolutionary algorithms for parameter optimization, Evolutionary Computation, 1, 1, 1-23 (1993) · doi:10.1162/evco.1993.1.1.1
[69] Cheng, X.; Jiang, M., An improved artificial bee colony algorithm based on Gaussian mutation and chaos disturbance, Lecture Notes in Computer Science, 7331, 1, 326-333 (2012)
[70] Higashi, N.; Iba, H., Particle swarm optimization with Gaussian mutation, Proceedings of the 2003 IEEE Swarm Intelligence Symposium, SIS 2003
[71] Jena, C.; Basu, M.; Panigrahi, C. K., Differential evolution with Gaussian mutation for combined heat and power economic dispatch, Soft Computing, 20, 2, 681-688 (2016) · doi:10.1007/s00500-014-1531-2
[72] Chang, C.; Lin, C., LIBSVM: a Library for support vector machines, ACM Transactions on Intelligent Systems and Technology, 2, 3, article 27 (2011) · doi:10.1145/1961189.1961199
[73] Salzberg, S. L., On comparing classifiers: pitfalls to avoid and a recommended approach, Data Mining and Knowledge Discovery, 1, 3, 317-328 (1997) · doi:10.1023/A:1009752403260
[74] Statnikov, A.; Tsamardinos, I.; Dosbayev, Y.; Aliferis, C. F., GEMS: a system for automated cancer diagnosis and biomarker discovery from microarray gene expression data, International Journal of Medical Informatics, 74, 7-8, 491-503 (2005) · doi:10.1016/j.ijmedinf.2005.05.002
[75] Fawcett, T., ROC graphs: Notes and practical considerations for researchers, Machine Learning, 31, 1-38 (2004)
[76] Gandomi, A. H.; Yang, X.-S.; Talatahari, S.; Alavi, A. H., Firefly algorithm with chaos, Communications in Nonlinear Science and Numerical Simulation, 18, 1, 89-98 (2013) · Zbl 1254.92089 · doi:10.1016/j.cnsns.2012.06.009
[77] Yang, X. S., Flower pollination algorithm for global optimization, Unconventional Computation and Natural Computation. Unconventional Computation and Natural Computation, Lecture Notes in Computer Science, 7445, 240-249 (2012), Berlin, Germany: Springer, Berlin, Germany · Zbl 1374.68527 · doi:10.1007/978-3-642-32894-7_27
[78] Yang, X.-S., A new metaheuristic bat-inspired Algorithm, Studies in Computational Intelligence, 284, 65-74 (2010) · Zbl 1197.90348 · doi:10.1007/978-3-642-12538-6_6
[79] Mirjalili, S., Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems, Neural Computing and Applications, 27, 1053-1073 (2016)
[80] Kennedy, J.; Eberhart, R., Particle swarm optimization, Proceedings of the IEEE International Conference on Neural Networks
[81] Derrac, J.; García, S.; Molina, D.; Herrera, F., A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms, Swarm and Evolutionary Computation, 1, 1, 3-18 (2011) · doi:10.1016/j.swevo.2011.02.002
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.