Towards Accurate Diabetes Prediction: A Logistic  Regression-Based Clinical Decision Support System

Author(s)	Mr. REGAMANDA LAKSHMAN SAI, Mr. PUSALA VENKATA SAI KIRAN
Country	India
Abstract	Early detection of diabetes is critical for effective management and prevention of severe complications. This project develops a predictive model using logistic regression to classify individuals as diabetic or non-diabetic based on clinical parameters such as glucose level, blood pressure, BMI, and age. The dataset is preprocessed to handle missing values and normalized for optimal model performance. The logistic regression algorithm, chosen for its interpretability and efficiency in binary classification, is trained and validated using standard machine-learning techniques. Evaluation through metrics like accuracy, precision, recall, and ROC-AUC demonstrates the model’s ability to provide reliable predictions. The proposed system offers a low-cost, data-driven tool that can assist healthcare professionals in early screening and risk assessment of diabetes.
Keywords	Diabetes Prediction Logistic Regression Machine Learning Clinical Decision Support System (CDSS) Healthcare Analytics Predictive Modeling Medical Diagnosis Data Mining Classification Algorithm Patient Health Data
Field	Engineering
Published In	Volume 7, Issue 6, November-December 2025
Published On	2025-11-16
DOI	https://doi.org/10.36948/ijfmr.2025.v07i06.60845

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