Implementing Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for Risk Assessment of Drug Interactions

Abstract

Novel drug development is time-consuming, difficult, and often unsuccessful. This has made combining therapies increasingly common and profitable in recent years. Healthcare professionals in the pharmaceutical sector are interested in the combination, but we must instantly solve drug-drug interactions. In few cases, single-perspective DDI evaluations are insufficient. Pharmacological therapy and patient safety depend on accurate drug interaction risk assessments. This work uses an Adaptive Neuro-Fuzzy Inference System (ANFIS) to assess and predict medication interaction hazards. The proposed method uses neural networks' generative skills and fuzzy logic to construct a robust decision-making framework that can handle drug interaction scenarios and their unpredictability. Fuzzy rules provide all the necessary risk assessment components. This includes dose, patient health issues, drug interaction profiles, and pharmacological properties. Accuracy, specificity, and Mean Squared Error evaluate the model after training with known drug interactions. These metrics evaluate model performance. These measures are essential for machine learning evaluation. The ANFIS-based model outperforms previous risk assessment methods in risk classification and prediction. This study found that the ANFIS architecture may improve medication management security and prevent dangerous drug interactions.