Policyholder cluster divergence based differential premium in diabetes insurance

[1]  Gerardo Beni,et al.  A Validity Measure for Fuzzy Clustering , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Ali Ouni,et al.  Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches † , 2018, Energies.

[3]  Rangaraj M. Rangayyan,et al.  Classification of breast masses in mammograms using genetic programming and feature selection , 2006, Medical and Biological Engineering and Computing.

[4]  Habibollah Haron,et al.  Supervised, Unsupervised, and Semi-Supervised Feature Selection: A Review on Gene Selection , 2016, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[5]  Joshua D. Woodard,et al.  Integrating high resolution soil data into federal crop insurance policy: Implications for policy and conservation , 2016 .

[6]  Lam,et al.  A Trimmed Clustering-Based l1-Principal Component Analysis Model for Image Classification and Clustering Problems with Outliers , 2019, Applied Sciences.

[7]  Arthur Charpentier,et al.  Personalization as a promise: Can Big Data change the practice of insurance? , 2020, Big Data Soc..

[8]  Jafar A. Alzubi,et al.  Assessing risk in life insurance using ensemble learning , 2019, J. Intell. Fuzzy Syst..

[9]  David A. Cather.  Reconsidering insurance discrimination and adverse selection in an era of data analytics , 2019 .

[10]  Douglas Steinley,et al.  K-means clustering: a half-century synthesis. , 2006, The British journal of mathematical and statistical psychology.

[11]  Jorge Cadima,et al.  Principal component analysis: a review and recent developments , 2016, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[12]  Michael I. Miller,et al.  A comparison of random forest variable selection methods for classification prediction modeling , 2019, Expert Syst. Appl..

[13]  R. Peter,et al.  Insurance Fraud in a Rothschild–Stiglitz World , 2018, Journal of Risk and Insurance.

[14]  G. Nijpels,et al.  Innovations in personalised diabetes care and risk management , 2019, European journal of preventive cardiology.

[15]  Anatoliy Swishchuk,et al.  Strong Law of Large Numbers and Central Limit Theorems for Functionals of Inhomogeneous Semi-Markov Processes , 2015 .

[16]  Calibrating intensities for long-term care multiple-state Markov insurance model , 2015 .

[17]  Fabio Baione,et al.  A health insurance pricing model based on prevalence rates: Application to critical illness insurance , 2014 .

[18]  Zhenhua Guo,et al.  Two-Dimensional Whitening Reconstruction for Enhancing Robustness of Principal Component Analysis , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Jens Perch Nielsen,et al.  Improving automobile insurance ratemaking using telematics: incorporating mileage and driver behaviour data , 2018, Transportation.

[20]  Li Zhang,et al.  Nonlinear feature selection using Gaussian kernel SVM-RFE for fault diagnosis , 2018, Applied Intelligence.

[21]  Martin Nell,et al.  Optimal Insurance Policy Indemnity Schedules with Policyholders’ Limited Liability and Background Risk , 2018 .

[22]  Marcus C. Christiansen,et al.  Multistate models in health insurance , 2012 .

[23]  Verónica Bolón-Canedo,et al.  Ensembles for feature selection: A review and future trends , 2019, Inf. Fusion.

[24]  Chih-Fong Tsai,et al.  Ensemble feature selection in high dimension, low sample size datasets: Parallel and serial combination approaches , 2020, Knowl. Based Syst..

[25]  Harun Uguz,et al.  A two-stage feature selection method for text categorization by using information gain, principal component analysis and genetic algorithm , 2011, Knowl. Based Syst..

[26]  Yifan Huang,et al.  Automobile insurance classification ratemaking based on telematics driving data , 2019, Decis. Support Syst..

[27]  W. V. D. Ven,et al.  Can premium differentiation counteract adverse selection in the Dutch supplementary health insurance? A simulation study , 2018, The European Journal of Health Economics.

[28]  Abdul Majid,et al.  Classification of stomach infections: A paradigm of convolutional neural network along with classical features fusion and selection , 2020, Microscopy research and technique.

[29]  J. Boone.  Basic Versus Supplementary Health Insurance: Moral Hazard and Adverse Selection , 2014 .

[30]  Jian Weng,et al.  Feature selection for text classification: A review , 2018, Multimedia Tools and Applications.

[31]  Alma Cohen,et al.  Testing for Adverse Selection in Insurance Markets , 2009 .

[32]  Verónica Bolón-Canedo,et al.  A comparison of performance of K-complex classification methods using feature selection , 2016, Inf. Sci..

[33]  Snaebjorn Gunnsteinsson.  Experimental identification of asymmetric information: Evidence on crop insurance in the Philippines , 2020, Journal of Development Economics.

[34]  N. Yuvaraj,et al.  Diabetes prediction in healthcare systems using machine learning algorithms on Hadoop cluster , 2017, Cluster Computing.

[35]  Sina Khanmohammadi,et al.  An improved overlapping k-means clustering method for medical applications , 2017, Expert Syst. Appl..

[36]  Moral Hazard and Advantageous Selection in Private Disability Insurance , 2018 .

[37]  D. P. Acharjya,et al.  Clustering Algorithm in Possibilistic Exponential Fuzzy C-Mean Segmenting Medical Images , 2017 .

[38]  Manoj Jayabalan,et al.  Risk prediction in life insurance industry using supervised learning algorithms , 2018, Complex & Intelligent Systems.

[39]  Zhenyu He,et al.  Joint sparse principal component analysis , 2017, Pattern Recognit..

[40]  R. Cattell.  The Scree Test For The Number Of Factors. , 1966, Multivariate behavioral research.

[41]  Shengwang Meng,et al.  Optimal insurance design under background risk with dependence , 2018 .

[42]  Jason H. Y. Wu,et al.  Association between Higher Blood Pressure and Risk of Diabetes Mellitus in Middle-Aged and Elderly Chinese Adults , 2019, Diabetes & metabolism journal.

[43]  Xianbiao Hu,et al.  The use of context-sensitive insurance telematics data in auto insurance rate making , 2018, Transportation Research Part A: Policy and Practice.

[44]  Mehrbakhsh Nilashi,et al.  An analytical method for diseases prediction using machine learning techniques , 2017, Comput. Chem. Eng..

[45]  Xin Liu,et al.  Can the development of a patient's condition be predicted through intelligent inquiry under the e-health business mode? Sequential feature map-based disease risk prediction upon features selected from cognitive diagnosis big data , 2020, Int. J. Inf. Manag..

[46]  Anil K. Jain.  Data clustering: 50 years beyond K-means , 2010, Pattern Recognit. Lett..

[47]  Li Li,et al.  Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records , 2016, Scientific Reports.

[48]  Majid Sarrafzadeh,et al.  A flexible data-driven comorbidity feature extraction framework , 2016, Comput. Biol. Medicine.