Clear Path Navigating Kidney Stone Risk with Precision Using Machine Learning

Chronic Kidney Disease (CKD) is a critical global health challenge associated with high morbidity and mortality rates, significantly impacting the quality of life and leading to severe complications, including the onset of other diseases. A major concern with CKD is its asymptomatic nature in the early stages, often resulting in delayed diagnosis and treatment. Early detection is vital as it enables timely medical intervention, which can slow disease progression and improve patient outcomes. This research explores the potential of machine learning (ML) to revolutionize CKD diagnosis by leveraging its ability to provide fast and accurate predictions, thus assisting healthcare professionals in making informed decisions. We utilized a publicly available CKD dataset from Kaggle, which presented challenges due to a substantial number of missing values. In real-world medical scenarios, incomplete data is common, often arising from patients missing specific measurements. To address this, we implemented an effective data preprocessing strategy. Missing values for numerical features were imputed using their mean values, while categorical (string) data was filled with the mode. This ensured a clean, comprehensive dataset, ready for robust model training. Four advanced machine learning algorithms Logistic Regression, Support Vector Machine (SVM), Random Forest Classifier, and Decision Tree Classifier were utilized to construct predictive models. The comparison of these models was made to identify the best-performing and accurate methodology for diagnosing CKD. Of these, the Random Forest Classifier performed better with the best accuracy, which underscores its capability to process intricate real-world medical data.