Abstract
The exposure of humans to toxic drug samples adversely affects lives of many beings. The drug molecule data is vast, complex and mostly unstructured. Big Data predictive analytics using machine learning techniques helps in analyzing such data and is currently an active area of research in biological computing. The objective of this research paper is to predict the toxicity of drug samples; here, the hot topic of the era comes into the role; machine learning plays a significant role in predicting toxicity of drug samples on the basis of various features of samples. The proposed framework predicts the toxicity of drug sample which can help in identifying adverse effects caused from it with an accuracy of 91.15% with random forest. The results are further optimized by building an ensemble of J48 and random forest, the two best performing classifiers on drug data. With a prediction accuracy of 96.20%, the results are compared with standard machine learning models like random forest, AdaBoost, Naive Bayes, etc., and are found to be much better than these classifiers. With the increase in toxicity in environment, this framework will play a significant role in improving lifestyle.
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Sharma, S., Hooda, N. (2020). Big Data Machine Learning Framework for Drug Toxicity Prediction. In: Chaudhary, A., Choudhary, C., Gupta, M., Lal, C., Badal, T. (eds) Microservices in Big Data Analytics. Springer, Singapore. https://doi.org/10.1007/978-981-15-0128-9_11
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DOI: https://doi.org/10.1007/978-981-15-0128-9_11
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