Integrated System for Multiple Disease Detection: A Performance Analysis

In today's healthcare landscape, catching chronic diseases like diabetes, chronic kidney disease, liver disease, and breast cancer early is vital to improving patient outcomes and cutting down long-term healthcare costs.Our project, "Integrated System for Multiple Disease Detection," uses machine learning to support this goal by applying powerful techniques like XGBoost and AdaBoost for more precise disease marker detection.By combining various data sources-such as biomarker analysis, imaging results, lab test outcomes, and patient health records-our system builds a complete picture of each patient's health.This integrated approach enhances the accuracy of disease detection, often spotting potential health concerns before symptoms appear, which allows for early detection and timely intervention.With a focus on chronic disease detection, our system helps shift healthcare toward proactive, preventative care.In the long run, this means not only lowering costs but also significantly improving patient outcomes, paving the way for a healthier future.