Diabetes Disease Analysis of Smart Healthcare System using Data Mining and Machine Learning
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Abstract
Diabetes is one of the most prevalent chronic diseases worldwide and poses significant challenges to healthcare systems due to its increasing incidence and associated complications. Early detection and accurate diagnosis are essential for effective disease management and reducing healthcare costs. This study presents a Diabetes Disease Analysis of Smart Healthcare System using Data Mining and Machine Learning techniques. The proposed framework utilizes healthcare data containing various clinical and physiological parameters such as glucose level, blood pressure, body mass index (BMI), insulin level, age, and family history. Data preprocessing techniques including data cleaning, normalization, and feature selection are applied to improve data quality and model performance. Various machine learning algorithms such as Logistic Regression, Support Vector Machine (SVM), Random Forest, Decision Tree, and XGBoost are employed for diabetes prediction and classification. The performance of these models is evaluated using metrics such as accuracy, precision, recall, F1-score, and confusion matrix. Experimental results demonstrate that ensemble-based approaches achieve superior predictive accuracy compared to conventional classifiers. The integration of data mining and machine learning within a smart healthcare environment enables efficient disease analysis, early risk identification, and decision support for healthcare professionals. The proposed system can assist in proactive diabetes management, improve patient outcomes, and contribute to the development of intelligent healthcare monitoring solutions.
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