AMR INSIGHT: MACHINE LEARNING-BASE MICROBE RESISTANCE PREDICTION USING ARFA

Abstract: Antimicrobial Resistance (AMR) has emerged as one of the most significant global healthcare challenges, leading to treatment failures, prolonged hospitalization, increased mortality rates, and rising healthcare costs. Conventional antimicrobial susceptibility testing methods require significant laboratory time and resources, making them unsuitable for rapid clinical decision-making. This paper proposes a Machine Learning-Based Antimicrobial Resistance Prediction System integrated with an Adaptive Resistance Fusion Algorithm (ARFA) to improve resistance prediction accuracy. The proposed framework utilizes clinical and microbiological data including patient demographics, microbial species information, infection characteristics, and antibiotic usage history. ARFA performs weighted feature fusion to generate a structured resistance risk representation before classification. Multiple supervised machine learning algorithms including Random Forest, Support Vector Machine (SVM), Logistic Regression, and Decision Tree are employed for resistance prediction. The system provides rapid resistance assessment, improves clinical decision support, and enhances antimicrobial stewardship practices. Experimental analysis demonstrates improved prediction performance and interpretability compared to traditional approaches. The proposed solution offers a scalable and intelligent framework for early antimicrobial resistance detection in modern healthcare environments.

Keywords: Antimicrobial Resistance, Machine Learning, ARFA, Random Forest, Support Vector Machine, Clinical Prediction, Healthcare Analytics, Antibiotic Resistance.