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Abstract : Chronic Kidney Disease (CKD) is a significant global health issue, often leading to
kidney failure and requiring costly medical treatments such as dialysis or transplants. Early
detection of CKD is essential for timely intervention and improved patient outcomes. This
project aims to develop a machine learning-based predictive model for diagnosing CKD at an
early stage. By utilizing a range of clinical features such as age, blood pressure, blood sugar, and
other relevant biomarkers, we employ machine learning algorithms, including Decision Trees,
Random Forests, and Support Vector Machines (SVM), to predict the likelihood of a patient
developing CKD. The dataset used in this study includes medical records of patients with various
kidney conditions, and preprocessing techniques such as normalization and missing data
handling are applied to ensure the model’s robustness. The performance of the model is
evaluated using metrics such as accuracy, precision, recall, and F1-score to ensure reliable
predictions. This approach not only aims to improve diagnostic accuracy but also provides a
data-driven solution to assist healthcare professionals in making informed decisions. The
outcome of this project can contribute to better management of CKD, ultimately helping to
reduce the burden on healthcare systems and improving patient care.
Keyword Chronic Kidney Disease, Machine Learning, Predictive Modeling, Decision Trees,
Random Forest, Support Vector Machine, Early Detection, Healthcare.
Reference: