Early Diagnostics Model for Dengue Disease Using Decision Tree-Based Approaches
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Sanjay Kumar Malik | Amita Malik | Yugal Kumar | Geeta Yadav | Shalini Gambhir | Y. Kumar | Amita Malik | S. K. Malik | G. Yadav | Shalini Gambhir | Geeta Yadav
[1] A. Chopra,et al. Effectiveness of Chloroquine and Inflammatory Cytokine Response in Patients With Early Persistent Musculoskeletal Pain and Arthritis Following Chikungunya Virus Infection , 2014, Arthritis & rheumatology.
[2] David A. Landgrebe,et al. A survey of decision tree classifier methodology , 1991, IEEE Trans. Syst. Man Cybern..
[3] Katechan Jampachaisri,et al. Analysis of significant factors for dengue fever incidence prediction , 2016, BMC Bioinformatics.
[4] Fatimah Ibrahim,et al. A noninvasive intelligent approach for predicting the risk in dengue patients , 2010, Expert Syst. Appl..
[5] Sellappan Palaniappan,et al. Intelligent heart disease prediction system using data mining techniques , 2008, 2008 IEEE/ACS International Conference on Computer Systems and Applications.
[6] Abdulkadir Sengür,et al. Effective diagnosis of heart disease through neural networks ensembles , 2009, Expert Syst. Appl..
[7] Claes Wohlin,et al. An evaluation of k-nearest neighbour imputation using Likert data , 2004 .
[8] K. Yu,et al. Metabonomic analysis of hepatitis B virus-induced liver failure: identification of potential diagnostic biomarkers by fuzzy support vector machine , 2008, Journal of Zhejiang University SCIENCE B.
[9] Gisele L. Pappa,et al. An Accurate Gaussian Process-Based Early Warning System for Dengue Fever , 2016, 2016 5th Brazilian Conference on Intelligent Systems (BRACIS).
[10] W. Loh,et al. SPLIT SELECTION METHODS FOR CLASSIFICATION TREES , 1997 .
[11] Yo-Ping Huang,et al. Using C-support vector classification to forecast dengue fever epidemics in Taiwan , 2016, 2016 International Conference on System Science and Engineering (ICSSE).
[12] P. Leitão,et al. Assessment of land use factors associated with dengue cases in Malaysia using Boosted Regression Trees. , 2014, Spatial and spatio-temporal epidemiology.
[13] Chung-Ho Hsieh,et al. Novel solutions for an old disease: diagnosis of acute appendicitis with random forest, support vector machines, and artificial neural networks. , 2011, Surgery.
[14] Gadadhar Sahoo,et al. Predication of Parkinson's disease using data mining methods: A comparative analysis of tree, statistical and support vector machine classifiers , 2012 .
[15] Fevzullah Temurtas,et al. Tuberculosis Disease Diagnosis Using Artificial Neural Networks , 2010, Journal of Medical Systems.
[16] Sanjay Kumar Malik,et al. PSO-ANN based diagnostic model for the early detection of dengue disease , 2017 .
[17] Paul E. Utgoff,et al. Incremental Induction of Decision Trees , 1989, Machine Learning.
[18] Peter D. Turney. Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm , 1994, J. Artif. Intell. Res..
[19] Yingtao Jiang,et al. A multilayer perceptron-based medical decision support system for heart disease diagnosis , 2006, Expert Syst. Appl..
[20] M. Cevdet Ince,et al. An expert system for detection of breast cancer based on association rules and neural network , 2009, Expert Syst. Appl..
[21] Lala Septem Riza,et al. A new approach on prediction of fever disease by using a combination of Dempster Shafer and Naïve bayes , 2016, 2016 2nd International Conference on Science in Information Technology (ICSITech).
[22] Ewout W Steyerberg,et al. Regression trees for predicting mortality in patients with cardiovascular disease: What improvement is achieved by using ensemble-based methods? , 2012, Biometrical journal. Biometrische Zeitschrift.
[23] Yugal Kumar,et al. Seminal quality prediction using data mining methods. , 2014, Technology and health care : official journal of the European Society for Engineering and Medicine.
[24] G. Sahoo,et al. Prediction of different types of liver diseases using rule based classification model. , 2013, Technology and health care : official journal of the European Society for Engineering and Medicine.
[25] D. Cummings,et al. Prediction of Dengue Incidence Using Search Query Surveillance , 2011, PLoS neglected tropical diseases.
[26] Akin Ozçift,et al. Random forests ensemble classifier trained with data resampling strategy to improve cardiac arrhythmia diagnosis. , 2011, Computers in biology and medicine.
[27] Sanjay Kumar Malik,et al. Role of Soft Computing Approaches in HealthCare Domain: A Mini Review , 2016, Journal of Medical Systems.
[28] Jens Myrup Pedersen,et al. A method for classification of network traffic based on C5.0 Machine Learning Algorithm , 2012, 2012 International Conference on Computing, Networking and Communications (ICNC).
[29] Plamena Andreeva,et al. Data Modelling and Specific Rule Generation via Data Mining Techniques , 2006 .
[30] M. Muniaraj. Fading chikungunya fever from India: beginning of the end of another episode? , 2014, The Indian journal of medical research.
[31] Fevzullah Temurtas,et al. Diagnosis of chest diseases using artificial immune system , 2012, Expert Syst. Appl..
[32] Nayyer Masood,et al. Dengue Fever Prediction: A Data Mining Problem , 2015 .
[33] Shinichiro Koga,et al. A prediction rule for the development of delirium among patients in medical wards: Chi-Square Automatic Interaction Detector (CHAID) decision tree analysis model. , 2013, The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry.
[34] Dongkyoo Shin,et al. A Comparative Study of Medical Data Classification Methods Based on Decision Tree and Bagging Algorithms , 2009, 2009 Eighth IEEE International Conference on Dependable, Autonomic and Secure Computing.
[35] B. Nongkynrih,et al. Malaria, dengue and chikungunya in India – An update , 2017 .