SPECTROSCOPIC BIOMARKER DETECTION FOR URINE DISEASES USING MACHINE LEARNING

Abstract: Urinary diseases such as kidney disorders, urinary tract infections, and diabetic nephropathy are becoming major health concerns worldwide. Early detection of these diseases is important to prevent severe complications and improve treatment outcomes. Traditional diagnostic methods are usually time-consuming, expensive, and dependent on laboratory testing. This paper proposes a machine learning–based framework for urine disease classification using spectroscopic biomarker analysis. The system analyses important urine parameters such as glucose, protein, pH, RBC, WBC, ketone, and bacteria to predict disease severity. Various machine learning algorithms including Support Vector Classifier, Logistic Regression, and Bernoulli Naive Bayes are used for prediction. The trained model is deployed using the Django framework for real-time disease prediction through a web application. The proposed system provides a rapid, accurate, non-invasive, and cost-effective solution for urine disease diagnosis and monitoring.

Keywords: Machine Learning, Urine Disease Classification, Spectroscopy, Biomarker Detection, Support Vector Machine, Non-Invasive Diagnosis.