Binary Fish School Search applied to feature selection: Application to ICU readmissions

This paper proposes a novel feature selection approach formulated based on the Fish School Search (FSS) optimization algorithm, intended to cope with premature convergence. In order to use this population based optimization algorithm in feature selection problems, we propose the use of a binary encoding scheme for the internal mechanisms of the fish school search, emerging the binary fish school search (BFSS). The suggested algorithm was combined with fuzzy modeling in a wrapper approach for Feature Selection (FS) and tested over three benchmark databases. This hybrid proposal was applied to an ICU (Intensive Care Unit) readmission problem. The purpose of this application was to predict the readmission of ICU patients within 24 to 72 hours after being discharged. We assessed the experimental results in terms of performance measures and the number of features selected by each used FS algorithms. We observed that our proposal can correctly select the discriminating input features.

[1]  Andries P. Engelbrecht,et al.  Computational Intelligence: An Introduction , 2002 .

[2]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[3]  Alexander Kossiakoff,et al.  Systems Engineering Principles and Practice , 2020 .

[4]  G.E. Moore,et al.  Cramming More Components Onto Integrated Circuits , 1998, Proceedings of the IEEE.

[5]  W. Baigelman,et al.  Patient readmission to critical care units during the same hospitalization at a community teaching hospital , 2005, Intensive Care Medicine.

[6]  B. Cuthbertson,et al.  Predicting death and readmission after intensive care discharge. , 2008, British journal of anaesthesia.

[7]  Hiroshi Motoda,et al.  Computational Methods of Feature Selection , 2022 .

[8]  Ronald L. Rivest,et al.  Constructing Optimal Binary Decision Trees is NP-Complete , 1976, Inf. Process. Lett..

[9]  Ying Tan,et al.  Feeding the Fish - Weight Update Strategies for the Fish School Search Algorithm , 2011, ICSI.

[10]  Nils J. Nilsson,et al.  Learning Machines: Foundations of Trainable Pattern-Classifying Systems , 1965 .

[11]  F. Glover.  HEURISTICS FOR INTEGER PROGRAMMING USING SURROGATE CONSTRAINTS , 1977 .

[12]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[13]  G.B. Moody,et al.  Robust parameter extraction for decision support using multimodal intensive care data , 2008, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[14]  Celso Leandro Palma,et al.  Principles of Modeling and Simulation: A Multidisciplinary Approach , 2009 .

[15]  A. Rosenberg,et al.  Patients readmitted to ICUs* : a systematic review of risk factors and outcomes. , 2000, Chest.

[16]  Amparo Alonso-Betanzos,et al.  Filter Methods for Feature Selection - A Comparative Study , 2007, IDEAL.

[17]  J. Grossman,et al.  Building a Better Delivery System: A New Engineering/Health Care Partnership , 2005 .

[18]  S. S. Iyengar,et al.  An Evaluation of Filter and Wrapper Methods for Feature Selection in Categorical Clustering , 2005, IDA.

[19]  Masoud Nikravesh,et al.  Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing) , 2006 .

[20]  Ronald L. Rivest,et al.  Training a 3-node neural network is NP-complete , 1988, COLT '88.

[21]  Uzay Kaymak,et al.  Fuzzy Decision Making in Modeling and Control , 2002, World Scientific Series in Robotics and Intelligent Systems.

[22]  Terrence J. Sejnowski,et al.  Analysis of hidden units in a layered network trained to classify sonar targets , 1988, Neural Networks.

[23]  D C Angus,et al.  Grappling with intensive care unit quality--does the readmission rate tell us anything? , 1998, Critical care medicine.

[24]  Jacek M. Zurada,et al.  Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance , 2008, Neural Networks.

[25]  João Miguel da Costa Sousa,et al.  Predicting Outcomes of Septic Shock Patients Using Feature Selection Based on Soft Computing Techniques , 2010, IPMU.

[26]  Abigail L. Horn,et al.  Multi-Objective Performance Evaluation Using Fuzzy Criteria: Increasing Sensitivity Prediction for Outcome of Septic Shock Patients , 2011 .

[27]  João Miguel da Costa Sousa,et al.  Decision tree search methods in fuzzy modeling and classification , 2007, Int. J. Approx. Reason..

[28]  Amar Partap Singh Pharwaha,et al.  Shannon and Non-Shannon Measures of Entropy for Statistical Texture Feature Extraction in Digitized Mammograms , 2009 .

[29]  Ramasamy Uthurusamy,et al.  Data mining and knowledge discovery in databases , 1996, CACM.

[30]  Chong Mun Ho,et al.  Classification and identification of frog sound based on entropy approach , 2011 .

[31]  Petra Perner,et al.  Data Mining - Concepts and Techniques , 2002, Künstliche Intell..

[32]  T. Warren Liao,et al.  Medical data mining by fuzzy modeling with selected features , 2008, Artif. Intell. Medicine.

[33]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[34]  G. F. Hughes,et al.  On the mean accuracy of statistical pattern recognizers , 1968, IEEE Trans. Inf. Theory.

[35]  C. Durbin,et al.  A case‐control study of patients readmitted to the intensive care unit , 1993, Critical care medicine.

[36]  James C. Bezdek,et al.  Validity-guided (re)clustering with applications to image segmentation , 1996, IEEE Trans. Fuzzy Syst..

[37]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[38]  Monique Pavel,et al.  Fundamentals of pattern recognition , 1989 .

[39]  R G Mark,et al.  MIMIC II: a massive temporal ICU patient database to support research in intelligent patient monitoring , 2002, Computers in Cardiology.

[40]  Subhash C. Bagui,et al.  Combining Pattern Classifiers: Methods and Algorithms , 2005, Technometrics.

[41]  C. Watts,et al.  A Systematic Review of Risk Factors and Outcomes , 2000 .

[42]  Robert Babuska,et al.  Fuzzy Modeling for Control , 1998 .

[43]  Pedro Larrañaga,et al.  A review of feature selection techniques in bioinformatics , 2007, Bioinform..

[44]  Padhraic Smyth,et al.  From Data Mining to Knowledge Discovery in Databases , 1996, AI Mag..

[45]  Bradley N. Doebbeling,et al.  Applying Systems Engineering Principles in Improving Health Care Delivery , 2007, Journal of General Internal Medicine.

[46]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[47]  Fernando Buarque de Lima Neto,et al.  A novel search algorithm based on fish school behavior , 2008, 2008 IEEE International Conference on Systems, Man and Cybernetics.

[48]  Dr. Zbigniew Michalewicz,et al.  How to Solve It: Modern Heuristics , 2004 .

[49]  João Miguel da Costa Sousa,et al.  Data mining using clinical physiology at discharge to predict ICU readmissions , 2012, Expert Syst. Appl..

[50]  R. Hayward,et al.  Who bounces back? Physiologic and other predictors of intensive care unit readmission , 2001, Critical care medicine.

[51]  D. Bates,et al.  The Costs of a National Health Information Network , 2005, Annals of Internal Medicine.

[52]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[53]  Fred W. Glover,et al.  Future paths for integer programming and links to artificial intelligence , 1986, Comput. Oper. Res..

[54]  Robert H. Brook,et al.  Assessing the Performance of Mortality Prediction Models , 1993 .