Neutrosophic rule-based prediction system for toxicity effects assessment of biotransformed hepatic drugs

Abstract

a b s t r a c tMeasuring toxicity is an important step in drug development. However, the current experimental methods which are used to estimate the drug toxicity are expensive and need high computational efforts. Therefore, these methods are not suitable for large-scale evaluation of drug toxicity. As a consequence, there is a high demand to implement computational models that can predict drug toxicity risks. In this paper, we used a dataset that consists of 553 drugs that biotransformed in the liver. In this data, there are four toxic effects, namely, mutagenic, tumorigenic, irritant and reproductive effects. Each drug is represented by 31 chemical descriptors. This paper proposes two models for predicting drug toxicity risks. The proposed models consist of three phases. In the first phase, the most discriminative features are selected using rough set-based methods to reduce the classification time and improve the classification performance. In the second phase, three different sam pling algorithms, namely, Random Under-Sampling, Random Over-Sampling, and Synthetic Minority Oversampling Technique (SMOTE) are used to obtain balanced data. In the third phase, the first proposed model employs the Neutrosophic Rule-based Classification System (NRCS), and the second model uses Genetic NRCS (GNRCS) to classify an unknown drug into toxic or non-toxic. The experimental results proved that the proposed models obtained high sensitivity (89-93%), specificity (91-97%), and GM (90-94%) for all toxic effects. Overall, the results of the proposed models indicate that it could be utilized for the prediction of drug toxicity in the early stages of drug development.

RETRACTED ARTICLE: Hybrid intelligent model for classifying chest X-ray images of COVID-19 patients using genetic algorithm and neutrosophic logic

“…As a simple example to illustrate the idea of using neutrosophic “IF-Then” rules, consider 8 samples from used dataset as follows (Basha et al. 2019 ): 0.0086542, −0.0038145, 0.0086542, , 2.04E-03, 0.0015298, Normal 0.006489, −0.00098806, 0.0065901, , 3.48E-03, 0.0018327, Normal 0.0015123, −0.002423, 0.0015123, , 2.98E-03, 0.0011059, Normal −8.35E-05, 1.31E-05, 8.35E-05, , 7.70E-03, 0.0026105, Normal 0.00065204, −0.0010234, 0.0009464 0.0068657, 0.0022366, Covid 0.00021982, 2.11E-05, 0.00032019 0.0018948, 0.0025601, Covid 0.0014582, −0.00020071, 0.0015333 0.0067872, 0.0019787, Covid 0.0013844, −0.0031614, 0.0013844 0.00059085, 0.00098422, Covid Divide these samples into training and testing sets and compute the membership degrees of each attribute. Examples of the generated “If-Then” rules for are: If A=<[High , 0, 0], [High, 0, 0], [High , 0, 0], , [Low , 0, 0],[Medium , 0, 0]>, then B=[Normal].…”

“…Many real-time applications as in (Basha et al. 2016b , 2017 , 2019 ; Anter and Hassenian 2019 , 2018 ; Gaber et al. 2015 ; Anter et al.…”

“…Many real-time applications as in (Basha et al 2016b(Basha et al , 2017(Basha et al , 2019Hassenian 2019, 2018;Gaber et al 2015;Anter et al 2014) use NS due to its powerful characteristics in treating any type of uncertainty.…”

Predictive Modeling of ICU Healthcare-Associated Infections from Imbalanced Data. Using Ensembles and a Clustering-Based Undersampling Approach

“…The problem of imbalanced data is very common in the medical field and has been addressed in some works, such as studies of mortality [12,31,32], treatment outcomes [33], drug toxicity assessment [34] and medical diagnosis [35][36][37]. Preliminary studies about the behavior of ensemble classifiers, as opposed to single classifiers in imbalanced data contexts, are conducted to predict the mortality of polytraumatized patients [12] and the success of non-invasive mechanical ventilation [33].…”

Neutrosophic structured element

“…Moreover, some generalization of neutrosophic sets, including interval neutrosophic set (Broumi & Smarandache, ; Gallego Lupiáñez, ; Garg, ; Liu & Shi, ; Ye, ), bipolar neutrosophic set (Broumi, Smarandache, Talea, & Bakali, ; Deli, Yusuf, Smarandache, & Ali, ; Uluçay, Deli, & Şahin, ), single‐valued neutrosophic set (Abdel‐Basset & Mohamed, ; Biswas, Pramanik, & Giri, ; Chakraborty et al, ; Edalatpanah, ; Liu & Wang, ; Şahin & Küçük, ; Ye, , ), simplified neutrosophic sets (Edalatpanah & Smarandache, ; Peng, Wang, Wang, Zhang, & Chen, ; Ye, , ), multi‐valued neutrosophic set (Ji, Zhang, & Wang, ; Peng, Wang, Wu, Wang, & Chen, ; Peng, Wang, & Yang, ), and neutrosophic linguistic set (Garg, ; Ma et al, ; Tian, Wang, Wang, & Zhang, ; Wang, Yang, & Li, ; Ye, ) have been presented. There are also various neutrosophic decision‐making models such as aggregation operator methods, TOPSIS, projection method, α‐cut set method, and so forth (see Abdel‐Basset, Manogaran, Gamal, & Smarandache, ; Basha, Tharwat, Abdalla, & Hassanien, ; Dhingra, Kumar, & Joshi, ; Guo & Cheng, ; Jha et al, a; Jha, Kumar, Priyadarshini, Smarandache, & Long, b; Kumar, Edalatpanah, Jha, Broumi, & Dey, , ; Rivieccio, ; Sert & Avci, ; Smarandache & Ali, ; Smarandache & Pramanik, ; Zhang, Zhang, & Cheng, ).…”