Machine Learning Prediction of Behavioral Changes Caused by Pharmaceutical Contaminants

Abstract

The contaminants from drugs have emerged as environmental pollutants because of the constant release of these compounds into the aquatic and terrestrial environments. Such biologically active compounds may influence the physiology and behavior of non-targeted animals through physiological modifications and behavioral modifications. Behavioral modifications such as mobility behavior, feeding behavior, and stress reactions are some of the major indicators of the effects of pharmaceutical compounds on the ecosystem. This paper suggests a machine learning approach for the prediction of behavior modifications of animals exposed to pharmaceutical pollutants by combining the environmental exposure conditions and behavioral modification indicators. Parameters such as the concentration of the pharmaceutical compound, exposure time period, temperature, pH, dissolved oxygen content, and behavioral modifications will be considered for the purpose of predictive analysis. The collected data were processed using various machine learning algorithms such as the Support Vector Machine algorithm, the Random Forest approach, and the Artificial Neural Network method. The efficiency of the algorithms was measured using accuracy, precision, recall, and F1-score. The analysis results showed that the impact of pharmaceutical pollution had a significant influence on the animal behavior patterns and that the increase of the contamination levels led to decreased activity and changed behavior. Among the tested algorithms, the suggested machine learning framework provided the best prediction results with the accuracy of 94.3%. It proves the possibility of recognizing the complicated relationships between contaminants and behavioral changes. The research suggests the potential of using artificial intelligence-based methods for the purposes of environmental monitoring and identification of the signs of pollution-induced stress among animals. Future research may utilize real-time sensor monitoring and behavioral data of multiple species for better environmental assessments