ASSESSING MACHINE LEARNING ALGORITHMS FOR CHRONIC DISEASE PREDICTION THROUGH PERFORMANCE METRICS
Keywords:
CNN, predictive models, ML algorithms, XGBoost, CD detectionAbstract
This research examines the efficacy of machine learning (ML) algorithms in the early detection and prediction of chronic diseases by utilizing a mix of structured and unstructured data. Chronic conditions such as diabetes and heart disease require advanced diagnostic methods due to their complex nature. Using algorithms like Support Vector Machines, Decision Trees, and Logistic Regression, this study aims to create predictive models that surpass traditional diagnostic methods. These models are rigorously tested with real-world data to ensure they are both accurate and practical for clinical use. The goal is to enhance early detection and management of chronic diseases, potentially reducing healthcare costs and improving patient outcomes. This innovative approach advances the application of artificial intelligence in healthcare, setting new standards for predictive diagnostics in chronic diseases.
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