Hybrid Model for Parkinson’s Disease Prediction

Parkinson’s is a chronic, progressive neurological disease with no known cause that affects the central nervous system of older people and compromises their movement. This disorder can impair daily aspects of people and therefore identify their existence early, helps in choosing treatments that can reduce the impact of the disease on the patient’s routine. This work aims to identify Parkinson’s traces through a voice recording replications database applied to a fuzzy neural network to identify their patterns and enable the extraction of knowledge about situations present in the data collected in patients. The results obtained by the hybrid model were superior to state of the art for the theme, proving that it is possible to perform hybrid models in the extraction of knowledge and the classification of behavioral patterns of high accuracy Parkinson’s.

[1]  Vanessa Souza Araujo,et al.  Incremental regularized Data Density-Based Clustering neural networks to aid in the construction of effort forecasting systems in software development , 2019, Applied Intelligence.

[2]  Paulo Vitor de Campos Souza,et al.  Regularized Fuzzy Neural Network Based on Or Neuron for Time Series Forecasting , 2018, NAFIPS.

[3]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[4]  Athanasios Tsanas,et al.  Developing a large scale population screening tool for the assessment of Parkinson's disease using telephone-quality voice , 2019, The Journal of the Acoustical Society of America.

[5]  N. Wood,et al.  Understanding the molecular causes of Parkinson's disease. , 2006, Trends in molecular medicine.

[6]  Sitanshu Sekhar Sahu,et al.  Parkinson disease prediction using intrinsic mode function based features from speech signal , 2020 .

[7]  S. Gilman,et al.  Diagnostic criteria for Parkinson disease. , 1999, Archives of neurology.

[8]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[9]  Daniel F. Leite,et al.  Ensemble of evolving data clouds and fuzzy models for weather time series prediction , 2018, Appl. Soft Comput..

[10]  C. S. George Lee,et al.  Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems , 1996 .

[11]  David Aldous,et al.  The Continuum Random Tree III , 1991 .

[12]  Antanas Verikas,et al.  Detecting Parkinson’s disease from sustained phonation and speech signals , 2017, PloS one.

[13]  Paulo Vitor de Campos Souza,et al.  Pulsar Detection for Wavelets SODA and Regularized Fuzzy Neural Networks Based on Andneuron and Robust Activation Function , 2019, Int. J. Artif. Intell. Tools.

[14]  Carlos J. Perez,et al.  Addressing voice recording replications for Parkinson's disease detection , 2016, Expert Syst. Appl..

[15]  Vanessa Souza Araujo,et al.  Pruning Fuzzy Neural Network Applied to the Construction of Expert Systems to Aid in the Diagnosis of the Treatment of Cryotherapy and Immunotherapy , 2019, Big Data Cogn. Comput..

[16]  D. Ruppert.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .

[17]  J. Balasubramaniam,et al.  On the distributivity of implication operators over T and S norms , 2004, IEEE Transactions on Fuzzy Systems.

[18]  Irena Rektorova,et al.  Speech disorders in Parkinson’s disease: early diagnostics and effects of medication and brain stimulation , 2017, Journal of Neural Transmission.

[19]  Andrew L. Maas.  Rectifier Nonlinearities Improve Neural Network Acoustic Models , 2013 .

[20]  C. Brayne,et al.  A systematic review of depression and mental illness preceding Parkinson's disease , 2006, Acta neurologica Scandinavica.

[21]  R. Tibshirani,et al.  Least angle regression , 2004, math/0406456.

[22]  L. Ramig,et al.  Intensive voice treatment in Parkinson disease: laryngostroboscopic findings. , 1995, Journal of voice : official journal of the Voice Foundation.

[23]  Geoffrey E. Hinton,et al.  Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.

[24]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[25]  R. Viswanathan,et al.  Complexity Measures of Voice Recordings as a Discriminative Tool for Parkinson’s Disease , 2019, Biosensors.

[26]  U. Roessmann,et al.  Parkinson disease, dementia, and alzheimer disease: Clinicopathological correlations , 1980, Annals of neurology.

[27]  Ronald R. Yager,et al.  Uninorm aggregation operators , 1996, Fuzzy Sets Syst..

[28]  Francis R. Bach,et al.  Bolasso: model consistent Lasso estimation through the bootstrap , 2008, ICML '08.

[29]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[30]  Plamen Angelov,et al.  Data density based clustering , 2014, 2014 14th UK Workshop on Computational Intelligence (UKCI).

[31]  Yongming Li,et al.  Classification of Parkinson’s disease utilizing multi-edit nearest-neighbor and ensemble learning algorithms with speech samples , 2016, BioMedical Engineering OnLine.

[32]  Pat Langley,et al.  Estimating Continuous Distributions in Bayesian Classifiers , 1995, UAI.

[33]  Vanessa Souza Araujo,et al.  Using Resistin, Glucose, Age and BMI and Pruning Fuzzy Neural Network for the Construction of Expert Systems in the Prediction of Breast Cancer , 2019, Mach. Learn. Knowl. Extr..

[34]  Carlos J. Perez,et al.  A two-stage variable selection and classification approach for Parkinson's disease detection by using voice recording replications , 2017, Comput. Methods Programs Biomed..

[35]  Lucas Oliveira Batista,et al.  Using hybrid systems in the construction of expert systems in the identification of cognitive and motor problems in children and young people , 2019, 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[36]  Ce Zhu,et al.  Early diagnosis of Parkinson's disease from multiple voice recordings by simultaneous sample and feature selection , 2019, Expert Syst. Appl..

[37]  Fernando Gomide,et al.  New uninorm-based neuron model and fuzzy neural networks , 2010, 2010 Annual Meeting of the North American Fuzzy Information Processing Society.

[38]  Mehrbakhsh Nilashi,et al.  An analytical method for diseases prediction using machine learning techniques , 2017, Comput. Chem. Eng..

[39]  Lucas Oliveira Batista,et al.  A Hybrid Model Based on Fuzzy Rules to Act on the Diagnosed of Autism in Adults , 2019, AIAI.

[40]  Tzyy-Ping Jung,et al.  Adaptive EEG-Based Alertness Estimation System by Using ICA-Based Fuzzy Neural Networks , 2006, IEEE Transactions on Circuits and Systems I: Regular Papers.