Abstract
Predictive analytics has gained a lot of interest in present era. Statistical methods and machine learning techniques are applied on historical data to build a predictive model to forecast and recommend future events. Boosting is an ensemble machine learning technique which combines several low accuracy models to create a high accuracy model. It can be utilized in various domains for improving prediction such as credit, insurance, consumer behavior, medical diagnosis, and sales. This paper evaluates the use of AdaBoost and Gradient Boosting ensemble machine learning techniques for two-class prediction. A publicly available diabetes data set is used to assess the proposed model accuracy. The experimental result shows that prediction accuracy of Gradient Boosting algorithm is better than traditional machine learning and AdaBoost algorithm to classify patients with diabetes using diabetes risk factors.
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References
E.B. Donald, A. Ahmed, Y.K.L. Raymond, Predictive analytics: introduction. IEEE Intell. Syst. 30, 6–8 (2015)
WHO-Diabetes programme Home Page. http://www.who.int/diabetes. Last accessed 17 Mar 2019
N. Jayanthi, B. VijayaBabu, N. Sambasiva Rao, Survey on clinical prediction models for diabetes prediction. J. Big Data 4(26), 1–15 (2017)
M. Fatima, M. Pasha, Survey of machine learning algorithms for disease diagnostic. J. Intell. Learn. Syst. Appl. 9, 1–16 (2017)
K. Kourou, T.P. Exarchos, K.P. Exarchos, M.V. Karamouzis, D.I. Fotiadis, Machine learning applications in cancer prognosis and prediction. Comput. Struct. Biotechnol. J. 13, 8–17(2015)
E.M. Hashem, M.S. Mabrouk, A study of support vector machine algorithm for liver disease. Am. J. Intell. Syst. 4(1), 9–14 (2014)
E.Z. Ferdousy, M.M. Islam, M.A. Matin, Combination of Naïve Bayes classifier and K-nearest neighbor (cNK) in the classification based predictive models. Comput. Inf. Sci. 6(3), 48–56 (2013)
X.Y. Qu, B. Yuan, W.H. Liu, A predictive model for identifying possible MCI to AD conversions in the ADNI database, in 2nd International Symposium Proceedings of the on Knowledge Acquisition and Modeling, vol. 105(3) (IEEE, Wuhan, P.R. China, 2009), pp. 102–105
Z.-H. Zhou, Ensemble Methods: Foundations and Algorithms, 1st edn. (Chapman & Hall/CRC, 2012)
L. Breiman, Bagging predictors. Mach. Learn. 24(2), 123–140 (1996)
S. Pouriyeh, S. Vahid, G. Sannino, G.D. Pietro, H. Arabnia, J. Gutierrez, A comprehensive investigation and comparison of machine learning techniques in the domain of heart disease, in 22nd IEEE Symposium on Computers and Communication Workshops—ICTS4eHealth (2017)
J.H. Friedman, Greedy function approximation: a gradient boosting machine. Ann. Stat. 29(5), 1189–1232 (2001)
Pima Indians Diabetes Database. https://www.kaggle.com/uciml/pima-indians-diabetes-database. Last accessed 17 Mar 2019
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Bahad, P., Saxena, P. (2020). Study of AdaBoost and Gradient Boosting Algorithms for Predictive Analytics. In: Singh Tomar, G., Chaudhari, N.S., Barbosa, J.L.V., Aghwariya, M.K. (eds) International Conference on Intelligent Computing and Smart Communication 2019. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-0633-8_22
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DOI: https://doi.org/10.1007/978-981-15-0633-8_22
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