Optimal Design of Interval Type-2 Fuzzy Heart Rate Level Classification Systems Using the Bird Swarm Algorithm

In this paper, the optimal designs of type-1 and interval type-2 fuzzy systems for the classification of the heart rate level are presented. The contribution of this work is a proposed approach for achieving the optimal design of interval type-2 fuzzy systems for the classification of the heart rate in patients. The fuzzy rule base was designed based on the knowledge of experts. Optimization of the membership functions of the fuzzy systems is done in order to improve the classification rate and provide a more accurate diagnosis, and for this goal the Bird Swarm Algorithm was used. Two different type-1 fuzzy systems are designed and optimized, the first one with trapezoidal membership functions and the second with Gaussian membership functions. Once the best type-1 fuzzy systems have been obtained, these are considered as a basis for designing the interval type-2 fuzzy systems, where the footprint of uncertainty was optimized to find the optimal representation of uncertainty. After performing different tests with patients and comparing the classification rate of each fuzzy system, it is concluded that fuzzy systems with Gaussian membership functions provide a better classification than those designed with trapezoidal membership functions. Additionally, tests were performed with the Crow Search Algorithm to carry out a performance comparison, with Bird Swarm Algorithm being the one with the best results.

[1]  Rajkumar Buyya,et al.  A Hybrid Bio-Inspired Algorithm for Scheduling and Resource Management in Cloud Environment , 2020, IEEE Transactions on Services Computing.

[2]  Patricia Melin,et al.  A hybrid model based on modular neural networks and fuzzy systems for classification of blood pressure and hypertension risk diagnosis , 2018, Expert Syst. Appl..

[3]  Ioan-Daniel Borlea,et al.  Model-Free Sliding Mode and Fuzzy Controllers for Reverse Osmosis Desalination Plants , 2018 .

[4]  Aboul Ella Hassanien,et al.  Chaotic Bird Swarm Optimization Algorithm , 2018, AISI.

[5]  Nadeem Javaid,et al.  Application of Bird Swarm Algorithm for Solution of Optimal Power Flow Problems , 2018, CISIS.

[6]  Yang Zhang,et al.  Variable Strength Combinatorial Test Data Generation Using Enhanced Bird Swarm Algorithm , 2018, 2018 19th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD).

[7]  Oscar Castillo,et al.  Optimization of Membership Function Parameters for Fuzzy Controllers of an Autonomous Mobile Robot Using the Flower Pollination Algorithm , 2018, J. Autom. Mob. Robotics Intell. Syst..

[8]  Oscar Castillo,et al.  Methodology for the Optimization of a Fuzzy Controller Using a Bio-inspired Algorithm , 2017, NAFIPS.

[9]  Mohammad Hossein Fazel Zarandi,et al.  A Type-2 Fuzzy Expert System for Diagnosis of Leukemia , 2017, NAFIPS.

[10]  Patricia Melin,et al.  A Hybrid Intelligent System Model for Hypertension Diagnosis , 2017, Nature-Inspired Design of Hybrid Intelligent Systems.

[11]  Ir. Riri Fitri Sari,et al.  "bioinspired algorithms for Internet of Things network" , 2017, 2017 4th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE).

[12]  Patricia Melin,et al.  Design of an Optimized Fuzzy Classifier for the Diagnosis of Blood Pressure with a New Computational Method for Expert Rule Optimization , 2017, Algorithms.

[13]  A. Gikas,et al.  An Unexpected Cause of Bradycardia in a Patient with Bacterial Meningitis , 2017, Case reports in medicine.

[14]  Yu Liu,et al.  A new bio-inspired optimisation algorithm: Bird Swarm Algorithm , 2016, J. Exp. Theor. Artif. Intell..

[15]  Mohammad Hossein Fazel Zarandi,et al.  A Computer-Aided Type-II Fuzzy Image Processing for Diagnosis of Meniscus Tear , 2016, Journal of Digital Imaging.

[16]  I. B. Turksen,et al.  From Type-I to Type-II Fuzzy System Modeling for Diagnosis of Hepatitis , 2016 .

[17]  Yang Liu,et al.  An Improved Genetic Algorithm with Initial Population Strategy for Symmetric TSP , 2015 .

[18]  B. Olshansky,et al.  Management of tachycardia , 2015, F1000prime reports.

[19]  V. Li,et al.  A social spider algorithm for global optimization , 2015, Appl. Soft Comput..

[20]  O. U. Oparaku,et al.  Fuzzy Logic System for Fetal Heart Rate Determination , 2015 .

[21]  Giuseppe Mancia,et al.  The Autonomic Nervous System and Hypertension , 2014, Circulation research.

[22]  Xin-She Yang,et al.  Flower pollination algorithm: A novel approach for multiobjective optimization , 2014, ArXiv.

[23]  Joseph Kobina Panford,et al.  Designing Algorithm for Malaria Diagnosis using Fuzzy Logic for Treatment (AMDFLT) in Ghana , 2014 .

[24]  G. Parati,et al.  Ambulatory blood pressure measurement: what is the international consensus? , 2013, Hypertension.

[25]  Jeroen J. Bax,et al.  2013 ESH/ESC guidelines for the management of arterial hypertension: the Task Force for the Management of Arterial Hypertension of the European Society of Hypertension (ESH) and of the European Society of Cardiology (ESC). , 2013, European heart journal.

[26]  I. Morsi,et al.  Fuzzy logic in heart rate and blood pressure measuring system , 2013, 2013 IEEE Sensors Applications Symposium Proceedings.

[27]  Mahmood Fathy,et al.  Protein structure prediction using bio-inspired algorithm: A review , 2012, The 16th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP 2012).

[28]  Patricia Melin,et al.  Modular Neural Networks and Type-2 Fuzzy Systems for Pattern Recognition , 2011, Studies in Computational Intelligence.

[29]  G. Mancia,et al.  Manual of Hypertension of the European Society of Hypertension, Second Edition , 2008 .

[30]  Bård Waldum.  Essential cardiology, principles and practice , 2006 .

[31]  A. Woods.  High blood pressure (hypertension). , 2002, Nursing.

[32]  T. Thien,et al.  [Ambulatory blood pressure measurement]. , 1998, Nederlands tijdschrift voor geneeskunde.

[33]  G. Prado-Arechiga,et al.  Oral-Poster Session.indd , 2018 .

[34]  Patricia Melin,et al.  Neuro-Fuzzy Hybrid Model for the Diagnosis of Blood Pressure , 2017, Nature-Inspired Design of Hybrid Intelligent Systems.

[35]  Patricia Melin,et al.  A Hybrid Intelligent System Model for Hypertension Risk Diagnosis , 2017, NAFIPS.

[36]  H. Koofigar,et al.  Training Echo Estate Neural Network Using Harmony Search Algorithm , 2017 .

[37]  Radu-Emil Precup,et al.  Grey Wolf Optimizer Algorithm-Based Tuning of Fuzzy Control Systems With Reduced Parametric Sensitivity , 2017, IEEE Transactions on Industrial Electronics.

[38]  Varinder Pabbi,et al.  Fuzzy Expert System for Medical Diagnosis , 2015 .

[39]  P. S. Mann,et al.  Diagnosis of Hypertension using Adaptive Neuro-Fuzzy Inference System , 2015 .

[40]  Patricia Melin,et al.  Design of a Fuzzy System for Diagnosis of Hypertension , 2015, Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization.

[41]  M. S. Ali,et al.  Design of fuzzy expert system for diagnosis of cardiac diseases , 2013 .

[42]  Raúl M. del Toro,et al.  OPTIMAL TUNING OF A NETWORKED LINEAR CONTROLLER USING A MULTI-OBJECTIVE GENETIC ALGORITHM AND ITS APPLICATION TO ONE COMPLEX ELECTROMECHANICAL PROCESS , 2009 .

[43]  Jeroen J. Bax,et al.  2007 ESH-ESC Guidelines for the management of arterial hypertension: the task force for the management of arterial hypertension of the European Society of Hypertension (ESH) and of the European Society of Cardiology (ESC). , 2007, Blood pressure.

[44]  Peter F. Fisher,et al.  Fuzzy sets , 1965 .