A Survey of Machine Learning Based Approaches for Parkinson Disease Prediction

Parkinson disease (PD) is a universal public health problem of massive measurement. Machine learning based method is used to classify between healthy people and people with Parkinson’s disease (PD). This paper presents a comprehensive review for the prediction of Parkinson disease buy using machine learning based approaches. The brief introduction of various computational intelligence techniques based approaches used for the prediction of Parkinson diseases are presented .This paper also presents the summary of results obtained by various researchers available in literature to predict the Parkinson diseases. Keywords— Parkinson’s disease, classification, random forest, support vector machine, machine learning, signal processing, artificial neural network.

[1]  Mehmet Can,et al.  Diagnosis of Parkinson’s Disease by Boosted Neural Networks , 2013, SOCO 2013.

[2]  Qiang Yang,et al.  MegaSNPHunter: a learning approach to detect disease predisposition SNPs and high level interactions in genome wide association study , 2009, BMC Bioinformatics.

[3]  Elmar Nöth,et al.  Detection of persons with Parkinson's disease by acoustic, vocal, and prosodic analysis , 2011, 2011 IEEE Workshop on Automatic Speech Recognition & Understanding.

[4]  Mehmet Can,et al.  Diagnosis of Parkinson’s Disease using Principal Component Analysis and Boosting Committee Machines , 2013, SOCO 2013.

[5]  Kenneth Revett,et al.  Feature selection in Parkinson's disease: A rough sets approach , 2009, 2009 International Multiconference on Computer Science and Information Technology.

[6]  Wen-Hung Chao,et al.  A vision-based analysis system for gait recognition in patients with Parkinson's disease , 2009, Expert Syst. Appl..

[7]  Betul Erdogdu Sakar,et al.  Telemonitoring of changes of unified Parkinson's disease rating scale using severity of voice symptoms , 2014 .

[8]  Arif Gülten,et al.  Classifier ensemble construction with rotation forest to improve medical diagnosis performance of machine learning algorithms , 2011, Comput. Methods Programs Biomed..

[9]  Lotfi Salhi,et al.  Voice Disorders Identification Using Multilayer Neural Network , 2010, Int. Arab J. Inf. Technol..

[10]  Rakesh Kumar Sinha,et al.  Artificial Neural Network based Classification of Neurodegenerative Diseases , 2013 .

[11]  C. Bielza,et al.  Predicting dementia development in Parkinson's disease using Bayesian network classifiers , 2013, Psychiatry Research: Neuroimaging.

[12]  M. Venkateswara Rao,et al.  Intelligent Parkinson Disease Prediction Using Machine Learning Algorithms , 2013 .

[13]  M. A. Chikh,et al.  Parkinson ’ s disease Detection With SVM classifier and Relief-F Features Selection Algorithm , 2014 .

[14]  Max A. Little,et al.  Novel Speech Signal Processing Algorithms for High-Accuracy Classification of Parkinson's Disease , 2012, IEEE Transactions on Biomedical Engineering.

[15]  R. Prashanth,et al.  Parkinson's disease detection using olfactory loss and REM sleep disorder features , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[16]  Austin H. Chen,et al.  The Improvement of Parkinson's Disease Classification using Genetic Algorithm-Random Forests and Genetic Algorithm-Support Vector Machine Methods , 2012 .

[17]  A. Cerasa,et al.  Machine learning on brain MRI data for differential diagnosis of Parkinson's disease and Progressive Supranuclear Palsy , 2014, Journal of Neuroscience Methods.

[18]  Mehmet Fatih Çağlar,et al.  Automatic Recognition of Parkinson's Disease from Sustained Phonation Tests Using ANN and Adaptive Neuro-Fuzzy Classifier , 2010 .

[19]  Resul Das,et al.  A comparison of multiple classification methods for diagnosis of Parkinson disease , 2010, Expert Syst. Appl..

[20]  Kapoor Tripti,et al.  Parkinson's disease Diagnosis using Mel-frequency Cepstral Coefficients and Vector Quantization , 2011 .

[21]  Mehmet Can,et al.  Diagnosis of Parkinson’s Disease using Fuzzy C-Means Clustering and Pattern Recognition , 2013, SOCO 2013.

[22]  Mohammad Ayache,et al.  Handwriting and Speech Prototypes of Parkinson Patients: Belief Network Approach , 2012 .

[23]  Shomona Gracia Jacob,et al.  Feature Relevance Analysis and Classification of Parkinson Disease Tele-Monitoring Data Through Data Mining Techniques , 2012 .

[24]  Tipu Z. Aziz,et al.  Prediction of Parkinson's Disease tremor Onset Using a Radial Basis Function Neural Network Based on Particle Swarm Optimization , 2010, Int. J. Neural Syst..

[25]  H. Hazan,et al.  Early diagnosis of Parkinson's disease via machine learning on speech data , 2012, 2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel.

[26]  Jihong Ouyang,et al.  An Efficient Diagnosis System for Parkinson's Disease Using Kernel-Based Extreme Learning Machine with Subtractive Clustering Features Weighting Approach , 2014, Comput. Math. Methods Medicine.

[27]  D. Lefebvre,et al.  A Preliminary Study of the Causality of Freezing of Gait for Parkinson's Disease Patients: Bayesian Belief Network Approach , 2013 .

[28]  Shianghau Wu,et al.  A Data Mining Analysis of The Parkinson’s Disease , 2011 .

[29]  Andrzej W. Przybyszewski,et al.  Applying Data Mining and Machine Learning Algorithms to predict symptom development in Parkinson's disease , 2014 .

[30]  Gang Wang,et al.  An efficient diagnosis system for detection of Parkinson's disease using fuzzy k-nearest neighbor approach , 2013, Expert Syst. Appl..

[31]  Faculty Member,et al.  A Decision Support System for Parkinson's Disease Diagnosis using Classification and Regression Tree , 2012 .

[32]  Amit Shukla,et al.  Understanding Postural Response of Parkinson's Subjects Using Nonlinear Dynamics and Support Vector , 2014 .

[33]  Mohamed Benyettou,et al.  Parkinson's Disease Recognition Using Artificial Immune System , 2011, J. Softw. Eng. Appl..

[34]  Concha Bielza,et al.  Unveiling relevant non-motor Parkinson's disease severity symptoms using a machine learning approach , 2013, Artif. Intell. Medicine.

[35]  Sanjay Jain,et al.  Design and Analysis of Data Mining Based Prediction Model forParkinson ’ s disease , 2014 .

[36]  Roman Cmejla,et al.  Acoustic analysis of voice and speech characteristics in early untreated Parkinson's disease , 2011, MAVEBA.

[37]  G. Sahoo,et al.  Predication of Parkinson's disease using data mining methods: A comparative analysis of tree, statistical and support vector machine classifiers , 2011, 2012 NATIONAL CONFERENCE ON COMPUTING AND COMMUNICATION SYSTEMS.

[38]  Danial Taheri Far,et al.  Speech Analysis for Diagnosis of Parkinson’s Disease Using Genetic Algorithm and Support Vector Machine , 2014 .

[39]  R. Geetha Ramani,et al.  Parkinson Disease Classification using Data Mining Algorithms , 2011 .

[40]  WangGang,et al.  An efficient diagnosis system for detection of Parkinson's disease using fuzzy k-nearest neighbor approach , 2013 .

[41]  V. Sellam,et al.  Classification of Normal and Pathological Voice Using SVM and RBFNN , 2014 .

[42]  Max A. Little,et al.  Nonlinear speech analysis algorithms mapped to a standard metric achieve clinically useful quantification of average Parkinson's disease symptom severity , 2011, Journal of The Royal Society Interface.

[43]  Veera Boonjing,et al.  Parkinsons Disease Classification using Neural Network and Feature Selection , 2012 .

[44]  Farhad Soleimanian Gharehchopogh,et al.  A Case Study of Parkinson's Disease Diagnosis using Artificial Neural Networks , 2013 .

[45]  Marius Ene,et al.  Neural network-based approach to discriminate healthy people from those with Parkinson's disease , 2008 .

[46]  Pasi Luukka,et al.  Feature selection using fuzzy entropy measures with similarity classifier , 2011, Expert Syst. Appl..

[47]  Jing Zhang,et al.  CSF multianalyte profile distinguishes Alzheimer and Parkinson diseases. , 2008, American journal of clinical pathology.