Hybrid artificial fish particle swarm optimizer and kernel extreme learning machine for type-II diabetes predictive model (original) (raw)
References
Cho N, Shaw JE, Karuranga S, Huang Y, da Rocha Fernandes JD, Ohlrogge AW, Malanda B (2018) IDF Diabetes Atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045. Diabetes Res Clin Pract 138:271–281 ArticleCAS Google Scholar
Bogatyrev SN (2016) Physical activity and type 2 diabetes mellitus risk: population studies review. Diabetes Mellitus 19(6):486–493 Article Google Scholar
Punthakee Z, Miller ME, Launer LJ, Williamson JD, Lazar RM, Cukierman-Yaffee T, Seaquist ER, Ismail-Beigi F, Sullivan MD, Lovato LC, Bergenstal RM (2012) Poor cognitive function and risk of severe hypoglycemia in type 2 diabetes: post hoc epidemiologic analysis of the ACCORD trial. Diabetes Care 35(4):787–793 Article Google Scholar
Bonds JA, Hart PC, Minshall RD, Lazarov O, Haus JM, Bonini MG (2016) Type 2 Diabetes Mellitus as a Risk Factor for Alzheimer’s Disease. In: Genes, Environment and Alzheimer's Disease. Academic Press, pp 387–413
American Diabetes Association (2020) 2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes—2020. Diabetes Care 43(Supplement 1):S14–S31 Article Google Scholar
World Health Organization (2011) . Use of glycated haemoglobin (HbA1c) in diagnosis of diabetes mellitus: abbreviated report of a WHO consultation. World Health Organization. https://apps.who.int/iris/handle/10665/70523
Abdul-Ghani MA, DeFronzo RA (2009) Plasma glucose concentration and prediction of future risk of type 2 diabetes. Diabetes Care 32(suppl 2):S194–S198 ArticleCAS Google Scholar
Nilashi M, Bin Ibrahim O, Ahmadi H, Shahmoradi L (2017) An analytical method for diseases prediction using machine learning techniques. Comput Chem Eng 106:212–223 ArticleCAS Google Scholar
Hassan BA, Rashid TA (2020) Operational framework for recent advances in backtracking search optimisation algorithm: A systematic review and performance evaluation. Appl Math Comput 370:124919
Jose J, Gautam N, Tiwari M, Tiwari T, Suresh A, Sundararaj V, Rejeesh MR (2021) An image quality enhancement scheme employing adolescent identity search algorithm in the NSST domain for multimodal medical image fusion. Biomed Signal Process Control 66:102480
Yang S, Wei R, Guo J, Xu L (2017) Semantic inference on clinical documents: combining machine learning algorithms with an inference engine for effective clinical diagnosis and treatment. IEEE Access 5:3529–3546 Article Google Scholar
Shalev-Shwartz S, Ben-David S (2014) Understanding machine learning: From theory to algorithms. Cambridge University Press, Cambridge Book Google Scholar
Kuhn M, Johnson K (2013) Applied predictive modeling(Vol. 26). Springer, New York Book Google Scholar
Devi RDH, Bai A, Nagarajan N (2020) A novel hybrid approach for diagnosing diabetes mellitus using farthest first and support vector machine algorithms. Obes Med 17:100152 Article Google Scholar
Lee BJ, Kim JY (2015) Identification of type 2 diabetes risk factors using phenotypes consisting of anthropometry and triglycerides based on machine learning. IEEE J Biomed Health Inform 20(1):39–46 Article Google Scholar
Lai H, Huang H, Keshavjee K, Guergachi A, Gao X (2019) Predictive models for diabetes mellitus using machine learning techniques. BMC Endocr Disord 19(1):1–9 Article Google Scholar
Orabi KM, Kamal YM, Rabah TM (2016) Early predictive system for diabetes mellitus disease. In: Industrial Conference on Data Mining. Springer, Cham, pp 420–427
Singh N, Singh P, Bhagat D (2019) A rule extraction approach from support vector machines for diagnosing hypertension among diabetics. Expert Syst Appl 130:188–205 Article Google Scholar
Singh N, Singh P (2020) Stacking-based multi-objective evolutionary ensemble framework for prediction of diabetes mellitus. Biocybern Biomed Eng 40(1):1–22 ArticleCAS Google Scholar
Xiong XL, Zhang RX, Bi Y, Zhou WH, Yu Y, Zhu DL (2019) Machine Learning Models in Type 2 Diabetes Risk Prediction: Results from a Cross-sectional Retrospective Study in Chinese Adults. Curr Med Sci 39(4):582–588 Article Google Scholar
Farran B, Channanath AM, Behbehani K, Thanaraj TA (2013) Predictive models to assess risk of type 2 diabetes, hypertension and comorbidity: machine-learning algorithms and validation using national health data from Kuwait—a cohort study. BMJ Open 3(5):e002457 Article Google Scholar
Farran B, AlWotayan R, Alkandari H, Al-Abdulrazzaq D, Channanath A, Thangavel AT (2019) Use of Non-invasive Parameters and Machine-Learning Algorithms for Predicting Future Risk of Type 2 Diabetes: A Retrospective Cohort Study of Health Data From Kuwait. Front Endocrinol 10:624 Article Google Scholar
Abdullah AS, Selvakumar S (2019) Assessment of the risk factors for type II diabetes using an improved combination of particle swarm optimization and decision trees by evaluation with Fisher’s linear discriminant analysis. Soft Comput 23(20):9995–10017 Article Google Scholar
Wu H, Yang S, Huang Z, He J, Wang X (2018) Type 2 diabetes mellitus prediction model based on data mining. Inform Med Unlocked 10:100–107 Article Google Scholar
Marateb HR, Mansourian M, Faghihimani E, Amini M, Farina D (2014) A hybrid intelligent system for diagnosing microalbuminuria in type 2 diabetes patients without having to measure urinary albumin. Comput Biol Med 45:34–42 ArticleCAS Google Scholar
Sundararaj V (2016) An efficient threshold prediction scheme for wavelet based ECG signal noise reduction using variable step size firefly algorithm. Int J Intell Eng Syst 9(3):117–126 Google Scholar
Vinu Sundararaj V (2019a) Optimal task assignment in mobile cloud computing by queue based Ant-Bee algorithm. Wirel Pers Commun 104(1):173–197 Article Google Scholar
Sundararaj V (2019b) Optimised denoising scheme via opposition-based self-adaptive learning PSO algorithm for wavelet-based ECG signal noise reduction. Int J Biomed Eng Technol 31(4):325–345 Article Google Scholar
Vinu S, Muthukumar S, Kumar RS (2018) An optimal cluster formation based energy efficient dynamic scheduling hybrid MAC protocol for heavy traffic load in wireless sensor networks. Comput Secur 77:277–288 Article Google Scholar
Sundararaj V, Anoop V, Dixit P, Arjaria A, Chourasia U, Bhambri P, MR, R. and Sundararaj, R. (2020) CCGPA-MPPT: Cauchy preferential crossover-based global pollination algorithm for MPPT in photovoltaic system. Prog Photovolt Res Appl 28(11):1128–1145 Article Google Scholar
Paolo M, Pio G, D’Elia D, Ceci M (2020) Exploiting transfer learning for the reconstruction of the human gene regulatory network. Bioinformatics 36(5):1553–1561 Google Scholar
Barracchia EP, Pio G, D’Elia D, Ceci M (2020) Prediction of new associations between ncRNAs and diseases exploiting multi-type hierarchical clustering. BMC Bioinform 21(1):1–24 Article Google Scholar
Eberhart, Shi Y (2001) Particle swarm optimization: developments, applications, and resources. In: Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546), Seoul, South Korea, vol 1, pp 81–86
Kennedy J, Eberhart RC, Shi Y (2001) The Particle Swarm, Swarm Intelligence, pp. 287–325.
Engelbrecht A (2012) Particle swarm optimization: Velocity initialization, 2012 IEEE Congress on Evolutionary Computation, Brisbane, QLD, pp. 1-8.
Neshat M, Sepidnam G, Sargolzaei M, Toosi AN (2014) Artificial fish swarm algorithm: a survey of the state-of-the-art, hybridization, combinatorial and indicative applications. Artif Intell Rev 42:965–997 Article Google Scholar
Chen H, Wang S, Li J, Li Y (2007) A Hybrid of Artificial Fish Swarm Algorithm and Particle Swarm Optimization for Feedforward Neural Network Training, Proceedings on Intelligent Systems and Knowledge Engineering (ISKE2007), 2007
Hoang N-D, Bui DT (2017) Slope Stability Evaluation Using Radial Basis Function Neural Network, Least Squares Support Vector Machines, and Extreme Learning Machine. In: Handbook of Neural Computation, pp 333–344 2017
Huang G, Huang G-B, Song S, You K (2015) Trends in extreme learning machines: A review. Neural Netw 61:32–48 Article Google Scholar
Borwein JM, Lewis AS (2000) Karush-Kuhn-Tucker Theory, Convex Analysis and Nonlinear Optimization, pp. 153–177.
Cawley GC, Talbot NLC (2007) Preventing over-fitting in model selection via Bayesian regularisation of the hyper-parameters. J Mach Learn Res 8:841–861 Google Scholar
Liu T, Hu L, Ma C, Wang Z-Y, Chen H-L (2015) A fast approach for detection of erythemato-squamous diseases based on extreme learning machine with maximum relevance minimum redundancy feature selection. Int J Syst Sci 46(5):919–931 Article Google Scholar
Zhao D, Huang C, Wei Y, Yu F, Wang M, Chen H (2016) An effective computational model for bankruptcy prediction using kernel extreme learning machine approach. Comput Econ:1–17
Smith JW, Everhart JE, Dickson WC, Knowler WC, Johannes RS (1988) Using the ADAP learning algorithm to forecast the onset of diabetes mellitus. In: Proceedings of the Symposium on Computer Applications and Medical Care. IEEE Computer Society Press, pp 261–265
Albina K, Lee SG (2019) Hybrid Stochastic Exploration Using Grey Wolf Optimizer and Coordinated Multi-Robot Exploration Algorithms. IEEE Access 7:14246–14255 Article Google Scholar
Deb K, Agrawal S, Pratap A, Meyarivan T (2000) A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimization: NSGA-II, Parallel Problem Solving from Nature PPSN VI Lecture Notes in Computer Science, pp. 849–858, 2000.
Oltean M, Grosan C, Abraham A, Koppen M (2005) Multiobjective optimization using adaptive Pareto archived evolution strategy, 5th International Conference on Intelligent Systems Design and Applications (ISDA'05), Warsaw, 2005, pp. 558-563.
Alharbi A, Alghahtani M (2018) Using Genetic Algorithm and ELM Neural Networks for Feature Extraction and Classification of Type 2-Diabetes Mellitus. Applied Artificial Intelligence, 1–18. https://doi.org/10.1080/08839514.2018.1560545
Liu L (2018) Advanced Biostatistics and Epidemiology Applied in Heart Failure Study. In: Heart Failure: Epidemiology and Research Methods, pp 83–102