An Artificial Neural Networks Model for Determining Feasible Side-effects of Antibacterial Drugson Domestic Animals

Abstract

The potential side-effects of Antibacterial drugs are a major concern during the human and veterinary therapy. It became essential for the clinicians to be aware of the side-effects of these drugs for ensuring the safety of the treating animal. Adverse reactions to antimicrobial agents consist of toxic effects mediated through chemical changes in tissue cells, sensitivity reactions produced by antigen-antibody reactions, and biological effects caused by alteration of the bacterial flora in the body. Artificial Neural network (ANN) has been successfully applied to find solutions for various real world problems in many fields including healthcare.  Medical diagnostic systems, biochemical analysis, and scanned image analysis are some of them where the medical practitioners couldn’t address alone. Neural networks are good at discovering and learning patterns, especially in large volumes of quantified data. The main aim of this research is to develop an accurate, cost-effective and easy-to-use decision support system to support the clinicians. Considering these facts, this research was developed to associate the antimicrobial use for treating the respective bacterial infections to their probable side-effects by using supervised neural network. The experimental results provide substantial evidence to identify the side effects of a specific drug.