International Journal of Advanced Research in Computer and Communication Engineering

Abstract: This research focuses on the development of a Smart Disease Prediction System capable of identifying multiple potential diseases from a single blood sample using machine learning techniques. The system analyzes critical blood biomarkers such as glucose, RBC count, WBC count, platelets, hemoglobin, cholesterol, triglycerides, creatinine, and additional biochemical indicators to classify health conditions with increased reliability. The proposed framework involves systematic data preprocessing, feature extraction, and multi-class classification using trained predictive models, enabling fast medical assessment with minimal manual intervention. A supervised learning model is trained on curated medical datasets and evaluated using accuracy, precision, recall, F1-score, and confusion matrix. The system generates disease risk output along with medically interpretable reasoning based on abnormal parameter deviation, making it useful for early diagnosis. Experimental results demonstrate that the system can detect diseases including diabetes, anemia, heart-related issues, high cholesterol, dengue, kidney disorder, and thyroid variations with promising prediction accuracy. This work aims to support healthcare systems through automation, improving diagnosis speed and reducing dependency on lab evaluation delays.

Keywords: Smart Disease Prediction System; Machine Learning; Blood Parameters; Multiclass Classification; Healthcare Automation; Early Diagnosis; Disease Identification; Biomedical Informatics.

Downloads: | DOI: 10.17148/IJARCCE.2025.141250