Predictive Analytics for Antibiotic Resistance Mitigation in Cloud-Powered Healthcare Systems Using Random Forest

Innovative antibiotic resistance mitigation measures are needed to protect world health. This research examines how cloud-powered healthcare systems might improve antibiotic resistance management using predictive analytics. A strong prediction model for antibiotic resistance trends is created using cloud-stored patient data and the Random Forest algorithm. The model analyzes relevant aspects using varied clinical and demographic information. The study uses a cloud-based infrastructure to scale and access patient information and antibiotic consumption trends. Random Forest's ensemble learning method accurately predicts antibiotic resistance development from complicated data linkages. The model's performance is rigorously tested and compared to older approaches. Results show that the suggested technique may identify antibiotic resistance tendencies early, giving healthcare professionals useful information for preemptive action. The cloud-based approach allows real-time data processing and decision-making. The model's interpretability helps healthcare providers identify reasons for resistance and develop focused interventions. By using cloud technologies and powerful machine learning to mitigate antibiotic resistance, this research advances healthcare analytics. The results show that predictive analytics may improve healthcare and emphasize the need for a proactive, data-driven antibiotic resistance strategy.