Rachel Sun¹ and Yujia Zhang², ¹Brookfield Academy, USA, ²California State Polytechnic University, USA

In the evolving landscape of healthcare, timely and accurate medical predictions are paramount, especially in managing chronic conditions like kidney disease. This paper introduces an innovative AI-driven application designed to enhance renal health management by predicting the need for dialysis and anemia, critical aspects of kidney care. Utilizing advanced algorithms such as Support Vector Machine (SVM) and XGBoost, coupled with cross-validation techniques, the application aims to provide reliable health predictions based on patient data. Challenges including model accuracy and processing speed were meticulously addressed through algorithm optimization and efficient data handling, ensuring the system's responsiveness to varying data complexities. Experimentation with mock patient scenarios revealed the system's capability to deliver precise anemia predictions and identify dialysis needs promptly, highlighting its potential in clinical settings. The application's blend of accuracy, speed, and user-centric design positions it as a valuable tool for patients and healthcare providers, promising to improve outcomes and decision-making in kidney health management.

Dialysis, Renal Health, AI, Monitoring