Abstract
Nowadays, the work pressure level increasing caused diseases of employees in the different organizations or companies. Predicting diseases using data mining techniques plays an important role of medical industry. The synthetic dataset named VASA which is collected the data from employees who affected the work pressure. In this paper, the capability of different classifiers such as J48 classifier, random forest classifier, and Naive Bayes classifier, analyzing the VASA dataset for disease prediction. The output of each classifier is compared with accuracy, TPR, TNR, precision, and error rate, and finally, get the best classifier which is produced high accuracy and low error rate.
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References
Rayka, S.S., Shet, V.N.: Cognitive analysis of data mining tools application in health care services. Published in IEEE (2020)
Beulah Christalin Latha, C., Carolin Jeeva, S.: Improving the accuracy of prediction of heart disease risk based on ensemble classification techniques. Inf. Med. Unlocked (2019)
Anitha, S., Vanitha, M.: The predicting diseases of employees with VASA dataset using entropy. Int. J. Adv. Sci. Technol. (IJAST) 29(4) (2020). ISSN: 2205-4238
Anitha, S., Vanitha, M.: Imputation methods for missing data for a proposed VASA dataset. Int. J. Innov. Technol. Explor. Eng. (IJITEE), 9(1) (2019). ISSN: 2278-3075
Anitha, S., Vanitha, M.: Selecting features related to work-pressure and assessing possible diseases in humans. Int. J. Sci. Technol. Res. 8(12) (2019). ISSN 2277-8616
Bharati, S., Rahman, M.A., Podder, P.: Breast cancer prediction applying different classification algorithm with comparative analysis using Weka. published in IEEE (2018)
Maliha, S.K., Ema, R.R., Ghosh, S.K.: Cancer disease prediction using Naive Bayes, K-nearest neighbor and J48 algorithm. published in IEEE (2019)
Mir, A, Dhage, S.N.: diabetes disease prediction using machine learning on big data of healthcare. published in IEEE (2018)
Deepa, B., Marseline, J.: Exploration of autism spectrum disorder using classification algorithms. Int. Conf. Recent Trends Adv. Comput. (2019)
Bhargava, N., Dayma, S., Kumar, A., Singh, P.: An approach for classification using simple CART algorithm in Weka. published in IEEE (2017) 978-1-5090-2717
Alam, M.Z., Saifur Rahman, M., Sohel Rahman, M.: A random forest based predictor for medical data classification using feature ranking. Inf. Med. Unlocked, Published by Elsevier Ltd (2019)
William, W., Ware, A., Habinka, A., Ejiri, B., Obungoloch, J.: Cervical cancer classification from Pap-mears using an enhanced fuzzy C-means algorithm. Published by Elsevier Ltd (2019)
Patil, B.M., Toshniwal, D., Joshi, R.C.: Predicting burn patient survivability using decision tree in WEKA environment. published in IEEE (2009)
Mienye, I.D., Sun, Y., Wang, Z.: Prediction performance of improved decision tree-based algorithms-a review. In: ScienceDirect, 2nd International Conference on Sustainable Materials Processing and Manufacturing. SMPM (2019)
Acknowledgements
This research work has been supported by RUSA PHASE 2.0, Alagappa University, Karaikudi.
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Anitha, S., Vanitha, M. (2022). Classification of VASA Dataset Using J48, Random Forest, and Naive Bayes. In: Satapathy, S.C., Peer, P., Tang, J., Bhateja, V., Ghosh, A. (eds) Intelligent Data Engineering and Analytics. Smart Innovation, Systems and Technologies, vol 266. Springer, Singapore. https://doi.org/10.1007/978-981-16-6624-7_28
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DOI: https://doi.org/10.1007/978-981-16-6624-7_28
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