SickleCare+: An AI-Powered Blood Report Analysis System for Sickle Cell Disease Management

Abstract

Sickle cell disease SCD) is a hereditary blood disorder requiring continuous monitoring through blood parameter analysis. This paper presents SickleCare+, an innovative AI- powered system that combines Optical Character Recognition OCR) technology with machine learning algorithms to analyze blood reports for sickle cell patients. The system addresses the critical gap between complex medical data interpretation and patient understanding by automatically extracting key parameters from uploaded blood test reports and providing AI- generated health insights and alerts. Our study verifies the integration of Tesseract OCR with advanced machine learning algorithms, where 95% accuracy in parameter extraction and 90% in abnormality detection is achieved. The system has been developed utilizing a MERN stack architecture along with MongoDB Atlas as the secured storage that is HIPAA-compliant and scalable. Clinical validation indicates enhanced early detection of possible complications, where notable health trend analysis and predictive alerts are offered by the system. This effort advances digital health innovation by bridging the gap in the availability of specialty care, most appropriate for low- resource environments where specialist reading may not be easily accessible.