Computer-assisted diagnosis of vertebral column diseases by adaptive neuro-fuzzy inference system

In this study, a clustering algorithm based on adaptive neural fuzzy inference system (ANFIS) was used for computer-assisted diagnosis of the vertebral column disorder from machine learning databases of UCI (University of California Irvine). Features of pelvic incidence, pelvic tilt and lumbar lordosis angle given in this dataset was applied to the inputs of the algorithm. The performance of algorithm was evaluated using mean square error and regression coefficient criteria to discriminate patients with vertebral column disease from healthy subjects. As a result, the classification performance of 90.82% was obtained by using the generated model of ANFIS.

[1]  S. B. Akben.  Importance of the shape and orientation of the spine and pelvis for the vertebral column pathologies diagnosis with using machine learning methods , 2016 .

[2]  E. Mizutani,et al.  Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review] , 1997, IEEE Transactions on Automatic Control.

[3]  Mahendra Bisen,et al.  HEART VALVE DISEASES DETECTION USING ANFIS AND WAVELET TRANSFORM , 2017 .

[4]  Jaime S. Cardoso,et al.  Diagnostic of Pathology on the Vertebral Column with Embedded Reject Option , 2011, IbPRIA.

[5]  Maria Claudia Bonfante,et al.  Fuzzy Classifier for the Diagnosis of Pathology on the Vertebral Column , 2014, IEEE Latin America Transactions.

[6]  Prasant Mohanty,et al.  Classification of EMG Signals Using ANFIS for the Detection of Neuromuscular Disorders , 2017 .

[7]  M. Ozer,et al.  Comparison of the Effects of Cross-validation Methods on Determining Performances of Classifiers Used in Diagnosing Congestive Heart Failure , 2015 .

[8]  G.A. Barreto,et al.  On the Application of Ensembles of Classifiers to the Diagnosis of Pathologies of the Vertebral Column: A Comparative Analysis , 2009, IEEE Latin America Transactions.

[9]  R. K. Tripathy,et al.  A Diagnostic System for Detection of Atrial and Ventricular Arrhythmia Episodes from Electrocardiogram , 2018 .

[10]  Mochammad Ariyanto,et al.  Electromyography (EMG) signal recognition using combined discrete wavelet transform based adaptive neuro-fuzzy inference systems (ANFIS) , 2017 .

[11]  Bekir Karlik,et al.  The role of data reduction for diagnosis of pathologies of the vertebral column by using supervised learning algorithms , 2015, 2015 XVIII International Conference on Soft Computing and Measurements (SCM).

[12]  Hiroki Tamura,et al.  Gaze Estimation Method Using Analysis of Electrooculogram Signals and Kinect Sensor , 2017, Comput. Intell. Neurosci..

[13]  Mohammad Reza Parsaei,et al.  EEG classification using recurrent adaptive neuro-fuzzy network based on time-series prediction , 2017, Neural Computing and Applications.

[14]  Zia Ul-Qayyum,et al.  Diagnosis of Vertebral Column Disorders Using Machine Learning Classifiers , 2013, 2013 International Conference on Information Science and Applications (ICISA).

[15]  H. Erdinc Kocer,et al.  Diagnosis of pathology on the vertebral column with backpropagation and Naive Bayes classifier , 2013, 2013 The International Conference on Technological Advances in Electrical, Electronics and Computer Engineering (TAEECE).

[16]  K. Polat,et al.  Classification of vertebral column disorders and lumbar discs disease using attribute weighting algorithm with mean shift clustering , 2016 .

[17]  Dervis Karaboga,et al.  Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey , 2018, Artificial Intelligence Review.