2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 17 , Issue 2 , PP: 36-49, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Dena Kadhim Muhsen 1 * , Bushra Fuaad Khmas 2 , Amjed Abbas Ahmed 3 , Ahmed T. Sadiq 4
Doi: https://doi.org/10.54216/JISIoT.170204
This study investigates the application of AI-powered predictive analytics in chronic disease management, focusing on the most effective machine learning models for predicting patient risk and optimizing healthcare interventions, like Random Forest, Linear Regression, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Gradient Boosting were evaluated using a dataset of 10,000 patient records. The models were assessed based on their accuracy, interpretability, and clinical relevance. Gradient Boosting attained the highest predictive accuracy, with an AUC of 0.89. Random Forest followed closely with an AUC of 0.85, offering a good balance of accuracy and interpretability. Linear Regression, with an AUC of 0.75, demonstrated the trade-offs between simplicity and model performance, while SVM and KNN performed with AUCs of 0.82 and 0.78, respectively, with SVM being robust but facing scalability challenges and KNN being less practical for large datasets. These AI models improve patient outcomes, decrease healthcare costs, and optimize healthcare delivery.
AI , Predictive analytics , Chronic disease management , Machine learning models , Personalized care
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