Life Lens-AI: Advanced AI-Driven Framework for Predicting and Preventing Kidney Stone Recurrence with Personalized Care
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Kidney stone disease continues to be a major global health challenge, largely due to its high recurrence rate even after successful surgical treatment. This study introduces LIFE Lens-AI, an intelligent framework designed to support both prediction and long-term prevention of kidney stone recurrence through the use of artificial intelligence.
The proposed system combines predictive analytics, medical image analysis, and personalized recommendation techniques within a single integrated platform. It leverages diverse data sources, including patient demographics, clinical history, lifestyle patterns, and medical imaging, to generate accurate and clinically meaningful insights.
The predictive module operates in two stages: recurrence risk prediction using XGBoost and surgical intervention prediction using a Random Forest classifier. The latter is modeled as a binary classification task, where labels are derived from established clinical treatment guidelines and historical decision patterns. Experimental results show that the system achieves an accuracy of 87.9% for recurrence prediction and 90.5% for surgical decision classification, supported by strong performance across precision, recall, F1-score, and AUC-ROC metrics.
Beyond prediction, the framework emphasizes patientcentric care by offering personalized dietary suggestions, specialist recommendations, and continuous monitoring support. The system is implemented as a secure webbased application with modern encryption and authentication mechanisms. Overall, LIFE Lens-AI provides a practical step toward proactive, AI-enabled healthcare and improved long-term patient outcomes.
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