Abstract
Data Mining (DM) is a helpful tool to extract and exploit the information from a large data set. There are different methods and algorithms available in data mining field. Several DM algorithms are used for classification such as Artificial Neural Network (ANN), K-Nearest Neighbor (K-NN), etc. The Decision Tree (DT) mining remains the best algorithm. In this paper, different classification methods including decision tree, C-RT, C5.0, AD-Tree and CS-MC4 algorithms are presented. These algorithms are evaluated using Recall, precision and F-measure. Experimental results show that AD-Tree is faster and present higher accuracy than the other classifier using a Diabetes data set.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Matuszewski, W., et al.: Prevalence of Diabetic Retinopathy in Type 1 and Type 2 Diabetes Mellitus Patients in North-East Poland. Medecina (2020)
Roy, M.S., et al.: The prevalence of diabetic retinopathy among adult type 1 diabetic persons in the United States. Arch. Ophthalmol. 122 (2004). (©2004 American Medical Association)
Wang, S.Y., Andrews, C.A., Herman, W.H., Gardner, T.W., Stein, J.D.: Incidence and Risk Factors for Developing Diabetic Retinopathy among Youths with Type 1 or Type 2 Diabetes throughout the United States, American society of ophthalmology (2017) https://doi.org/10.1016/j.ophtha.2016.10.031
Fiarni, C., Sipayung, E.M., Maemunah, S.: Analysis and prediction of diabetes complication disease using data mining algorithm. In: The Fifth Information Systems International Conference 2019, Science Direct. Procedia Computer Science, vol. 161, pp. 449–457 (2019)
Gárate-Escamila, A..K.., Hassani, A..H..E.., Andrès, E..: Classification models for heart disease prediction using feature selection and PCA. Inf. Med. Unlock. 19, 100330 (2020). https://doi.org/10.1016/j.imu.2020.100330
Mujumdar, A., Vaidehi, V.: Diabetes prediction using machine learning algorithms. In: International Conference on Recent Trends in Advanced Computing 2019, ICRTAC 2019 (2019)
Ghosh, P., Azam, A., Karim, A., Hassan, M., Roy, K., Jonkman, M.: A comparative study of different machine learning tools in detecting diabetes. 25th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems. Procedia Comput. Sci. 192, 467–477 (2021)
Viloria, A., Herazo-Beltran, Y., Cabrera, D., Pineda, O.B.: Diabetes diagnostic prediction using vector support machines. In: The 11th International Conference on Ambient Systems, Networks and Technologies (ANT), 6–9 April 2020, Warsaw, Poland (2020)
Zhang, X., Xiao, H., Gao, R., Zhang, H., Wang, Y.: K-nearest neighbors rule combining prototype selection and local feature weighting for classification. Knowl. Based Syst. 243 (2022)
Patel, B.R., Rana, K.K.: A survey on decision tree algorithm for classification. Int. J. Eng. Dev. Res. 2(1) (2014)
Sisodia, D., Sisdia, D.S.: Prediction of diabetes using classification algorithms. In: International Conference on Computational Intelligence and Data Sciences (ICCIDS), Science Direct Procedia Computer Science, vol. 132, pp. 1578–1585 (2018)
Harz, H.H., Rafi, A.O., Hijazi, M.O., Abu-Naser, S.S.: Artifical neural network for diabetes using JNN. Int. J. Acad. Eng. Res. 4(10), 14–22 (2020)
Liu, J., Tang, Z.H., Zeng, F., Li, Z., Zhou, L.: Artificial neural network models for prediction of cardiovascular autonomic dysfunction in general Chinese population. BMC Med. Inf. Dec. Mak. 13(1) (2013). https://doi.org/10.1186/1472-6947-13-80
Pradhan, N., Rani, G., Dhaka, V.S., Poonia, R.C.: Diabetes prediction using artificial neural network. Deep Learn. Tech. Biomed. Health Inf. 121, 327–339 (2020). https://doi.org/10.1016/B978-0-12-819061-6.00014-8
Temurtas, H., Yumusak, N., Temurtas, F.: A comparative study on diabetes disease diagnosis using neural networks. Expert Syst. Appl. 36(4), 8610–8615 (2009). https://doi.org/10.1016/j.eswa.2008.10.032
Sharma, A.K., Sahni, S.: A comparative study of classification algorithms for spam email data analysis. Int. J. Comput. Sci. Eng. 3(5), 1890–1895 (2011)
Nemae, D.R., Gupa, R.K.: Diabetes prediction using BPSO and decision tree classifier. In: 2nd International Conference on Data, Engineering and Applications (IDEA), IEEE Xplore 2020 (2020)
Nancy, P., Ramani, R.G., Jacob, S.G.: Discovery of gender classification rules for social network data using data mining algorithms. In: Proceedings of the IEEE International Conference on Computational Intelligence and Computing Research (ICCIC 2011); Kanyakumari, India (2011)
Yuvaraj, N., Chang, V., Pinagapani, A., Kannan, S., Dhiman, G., Rajan, A.R.: Automatic detection of cyberbullying using multi-feature based artificial intelligence with deep decision tree classification, Elsevier. Comput. Electric. Eng. 92 (2021)
Kumar, B.M., Perumal, R.S., Nadesh, R.K., Arivuselvan, K.: Type 2: diabetes mellitus prediction using Deep Neural Networks classifier. Int. J. Cogn. Comput. Eng. 1, 55–61 (2020)
Strzelecka, A., Zawadzka, D.: Application of classification and regression tree (CRT) analysis to identify the agricultural households at risk of financial exclusion. Procedia Comput. Sci. 192, 4532–4541 (2021)
Sharma, S., Agrawal, J., Sharma, S.: Classification through Machine Learning Technique: C4.5 Algorithm based on Various Entropies No 16 (2013)
Domingos, P.: MetaCost: a general method for making classifiers cost-sensitive. In: Proceedings of the Fifth International Conference on Knowledge Discovery and Data Mining, pp. 155–164. ACM Press, San Diego, CA (1999)
Chawla, N.V., Japkowicz, N., Kolcz, A. (eds.) Special Issue on Learning from Imbalanced Datasets. SIGKDD, vol. 6, issue 1. ACM Press (2004)
Zubek, V.B., Dietterich, T.: Pruning improves heuristic search for cost-sensitive learning. In: Proceedings of the Nineteenth International Conference of Machine Learning, pp. 27–35, Morgan Kaufmann, Sydney, Australia (2002)
Madadipouya, K.: A new decision tree method for data mining in medicine. Adv. Comput. Intell. Int. J. 2(3) (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Fakir, Y., Abdelmotalib, N. (2022). Analysis of Decision Tree Algorithms for Diabetes Prediction. In: Fakir, M., Baslam, M., El Ayachi, R. (eds) Business Intelligence. CBI 2022. Lecture Notes in Business Information Processing, vol 449. Springer, Cham. https://doi.org/10.1007/978-3-031-06458-6_16
Download citation
DOI: https://doi.org/10.1007/978-3-031-06458-6_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-06457-9
Online ISBN: 978-3-031-06458-6
eBook Packages: Computer ScienceComputer Science (R0)