CiteScore: 4.9     h-index: 21

Document Type : Original Research Article


1 Department of Chemical and Petroleum Engineering, Sharif University of Technology, Tehran, Iran

2 Center for Process Design, Safety and Loss Prevention (CPSL), Sharif University of Technology, Tehran, Iran

3 Department of Energy Engineering, Sharif University of Technology, Tehran, Iran


In this work, a new multi-class classification approach was employed in the QSAR model to assess chemical toxicity prediction through handling the imbalanced dataset as the critical preprocessing step in the training dataset. Various classifiers of the decision tree, K-NN, naive Bayes, kernelled naive Bayes, and SVM and two distinct acute aquatic toxicity datasets towards Daphnia Magna and Fathead Minnow Fish were used to evaluate the generality of the approach. The quantitative response (LC50) was discretized into ten bins. Imbalanced dataset classification leads to a high level of errors since the classifier tends to learn from the majority class more than the minority class. Each training dataset was specified by different weights related to the class population. These datasets were then bootstrapped based on their weights to convert the imbalanced dataset into a balanced one. This approach enhanced the accuracy of classification of material toxicity dramatically (up to 99%). Balanced dataset classification had high overall accuracy when correlated attributes were removed. Therefore, fewer attributes are sufficient to predict material toxicity.The overall accuracy improvement of the decision tree, K-NN, naive Bayes, kernelled naive Bayes, and SVM for the Daphnia Magna dataset after balancing the data set are 58.03%, 55.08%, 9.09%, 72.48%, and 53.05%, respectively.

Graphical Abstract

Kernelled Naive Bayes Using a Balanced Dataset for Accurate Classification of the Material Toxicity


[1] M. Rausand, Risk assessment: theory, methods, and applications, John Wiley & Sons, 2013.
[2] G. Popov, B.K. Lyon, B. Hollcroft, John Wiley & Sons, 2016.
[3] Development (OECD) Staff, Development. Working Party on Environmental Performance, & United Nations. Economic Commission for Europe. Committee on Environmental Policy. OECD environmental performance reviews: Canada, 2004.
[4] CEC, Regulation (EC) No. 1907/2006 of the European Parliament and of the Council of 18 December 2006 concerning the Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH), EU CEC Brussels, 2006.
[5] M.T. Cronin, J.D. Walker, J.S. Jaworska, M.H. Comber, C.D. Watts, A.P. Worth, Environ. Health Perspect., 2003, 111, 1376–1390.
[6] R. Combes, C. Grindon, M.T. Cronin, D.W. Roberts, J.F. Garrod, Alt. Lab. Anim., 2008, 36, 45–63.
[7] M. Cronin, Chemical Toxicity Prediction: Category Formation and Read-Across, Royal Society of Chemistry, 2013, pp 155–167.
[8] H. Liu, E. Papa, P. Gramatica, Chem. Res. Toxicol., 2006, 19, 1540–1548.
[9] T.M. Mitchell, Machine Learning, McGraw-Hill, 1997.
[10] D.E. Jones, H. Ghandehari, J.C. Facelli, Comput. Methods Programs Biomed., 2016, 132, 93–103.
[11] H. Yang, L. Sun, W. Li, G. Liu, Y. Tang, Front. Chem., 2018, 6, 30.
[12] A.A. Toropov, A.P, Toropova, I. Raska Jr, D. Leszczynska, J. Leszczynski, Comput. Biol. Med., 2014, 45, 20–25.
[13] S. Schmidt, M. Schindler, D. Faber, J. Hager, SAR QSAR Environ. Res., 2021, 32, 151–174.
[14] O. Tinkov, V.Y. Grigorev, A.N. Razdolsky, L.D. Grigoryeva, J.C. Dearden, SAR QSAR Environ. Res., 2020, 31, 615–641.
[15] F. Lunghini, G. Marcou, P. Azam, M.H. Enrici, E. Van Miert, A. Varnek, SAR QSAR Environ. Res., 2020, 31, 655–675.
[16] M. Marzo, G.J. Lavado, F. Como, A.P. Toropova, A.A. Toropov, D. Baderna, C. Cappelli, E. Benfenati, SAR QSAR Environ. Res., 2020, 31, 227–243.
[17] J. Polanski, Chemoinformatics: From Chemical Art to Chemistry in Silico, Elsevier, 2019, pp 601–618.
[18] S.R. Kazmi, R. Jun, M.S. Yu, C. Jung, D. Na, Comput. Biol. Med., 2019, 106, 54–64.
[19] Y. Wu, G. Wang, Int. J. Mol. Sci., 2018, 19, 2358.
[20] S.J. Russell, P. Norvig, Artificial Intelligence-A Modern Approach, 3rd Ed., Pearson Education: London, 2010.
[21] E. Alpaydin, Introduction to machine learning, MIT press, 2020.
[22] M. Mohri, A. Rostamizadeh, A. Talwalkar, Foundations of Machine Learning, MIT Press, 2018.
[23] S. Cassani, S. Kovarich, E. Papa, P.P. Roy, L. van der Wal, P. Gramatica, J. Hazard. Mater., 2013, 258, 50–60.
[24] F. Abbasitabar, V. Zare-Shahabadi, Chemosphere, 2017, 172, 249–259.
[25] B. Giner, C. Lafuente, D. Lapeña, D. Errazquin, L. Lomba, Ecotoxicol. Environ. Saf., 2020, 191, 110004.
[26] R. Aalizadeh, C. Peter, N.S. Thomaidis, Environ. Sci. Process Impacts, 2017, 19, 438–448.
[27] T. Fan, G. Sun, L. Zhao, X. Cui, R. Zhong, Int. J. Mol. Sci., 2018, 19, 3015.
[28] H. Zhang, P. Yu, J.X. Ren, X.B. Li, H.L., Wang, L. Ding, W.B. Kong, Food Chem. Toxicol., 2017, 110, 122–129.
[29] H. Zhang, C. Shen, R.Z. Liu, J. Mao, C.T. Liu, B. Mu, J. Appl. Toxicol., 2020,40, 1198–1209.
[30] A. Lillicrap, S.J. Moe, R. Wolf, K.A. Connors, J.M. Rawlings, W.G. Landis, S.E. Belanger, Integr. Environ. Assess. Manag., 2020, 16, 452–460.
[31] J. Roy, P.K. Ojha, E. Carnesecchi, A. Lombardo, K. Roy, E. Benfenati, J. Hazard. Mater., 2020, 386, 121660.
[32] N. Abramenko, L. Kustov, L. Metelytsia, V. Kovalishyn, I. Tetko, W. Peijnenburg, J. Hazard. Mater., 2020, 384, 121429.
[33] L. Yang, Y. Wang, J. Chang, Y. Pan, R. Wei, J. Li, H. Wang, Chemosphere, 2020, 258, 127217.
[34] K. Khan, P.M. Khan, G. Lavado, C. Valsecchi, J. Pasqualini, D. Baderna, E. Benfenati, Chemosphere, 2019, 229, 8–17.
[35] S.K. Pandey, P.K. Ojha, K. Roy, Chemosphere, 2020, 252, 126508.
[36] X. Jin, M. Jin, L. Sheng, Comput. Biol. Med., 2014, 51, 205–213.
[37] J. Han, J. Pei, M. Kamber, Data Mining: Concepts and Techniques, Elsevier Science, ProQuest Ebook Central, 2011.
[38] M. Kantardzic, Data Mining: Concepts, Models, Methods, and Algorithms, 2nd Ed., IEEE:  Wiley, 2019.
[39] A. Pérez, P. Larrañaga, I. Inza. Int. J. Approx. Reason., 2009, 50, 341–362.
[40] G.H. John, P. Langley, Estimating continuous distributions in Bayesian classifiers. Morgan Kaufmann: Montr´eal, 1995, pp 338–345.
[41] K.P. Murphy, Machine Learning: A Probabilistic Perspective, MIT Press: Cambridge, 2012.
[42] L. Michielan, L. Pireddu, M. Floris, S. Moro, Mol. Inform., 2010, 29, 51–64.
[43] V. Kotu, B. Deshpande, Predictive analytics and data mining: concepts and practice with rapidminer, Elsevier Science, Morgan Kaufmann: USA, 2014.
[44] N. Japkowicz, S. Stephen, Intell. Data Anal., 2002, 6, 429–449.
[45] S. Barua, M.M. Islam, X. Yao, K. Murase, IEEETrans. Knowl. Data Eng., 2012, 26, 405–425.
[46] C.X. Ling, C. Li. Data mining for direct marketing: Problems and solutions, AAAI Press: New York, 1998, pp 73–79.
[47] A. Fernández, S. Garcia, F. Herrera, N.V. Chawla, J. Artif. Intell. Res, 2018, 61, 863-905.
[48] Z.H. Zhou, X.Y. Liu, Comput. Intell., 2010, 26, 232–257.
[49] A. Fernández, V. López, M. Galar, M.J. Del Jesus, F. Herrera, Knowl.-Based Syst., 2013, 42, 97–110.
[50] D.M. Hawkins, S.C. Basak, D. Mills, J. Chem. Inform. Comput. Sci., 2003, 43, 579–586.
[51] S.K. Jha, T.H. Yoon, Z. Pan, Comput. Biol. Med., 2018, 99, 161–172.
[52] A. Rácz, D. Bajusz, K. Héberger, Mol. Inform., 2019, 38, 1800154.
[53] R. Todeschini, V. Consonni, Handbook of molecular descriptors, Wiley-VCH: Weinheim, 2008.
[54] M. Cassotti, D. Ballabio, V. Consonni, A. Mauri, I.V. Tetko, R. Todeschini, Altern. Lab. Anim., 2014, 42, 31–41.
[55] M. Cassotti, D. Ballabio, R. Todeschini, V. Consonni, SAR QSAR Environ. Res., 2015, 26, 217–243.
[56] D. Dua, C. Graff, UCI Machine Learning Repository. School of Information and Computer Science, University of California, Irvine, CA, USA. 2019.
[57] G.E. Jensen, J.R. Niemelä, E.B. Wedebye, N.G. Nikolov, SAR QSAR Environ. Res., 2008, 19, 631–641.
[58] M.T. Martin, T.B. Knudsen, D.M. Reif, K.A. Houck, R.S. Judson, R.J. Kavlock, D.J. Dix,  Biol. Reprod. 2011, 85, 327–339.
[59] C. Jiang, H. Yang, P. Di, W. Li, Y. Tang, G. Liu, J. Appl. Toxicol., 2019, 39, 844–854.