Chronic Kidney Diseases Prediction Using K-Means Algorithm

This transformation allows the model to capture the relationships between different health indicators and their correlation with CKD risk. At the core of the predictive analysis lies a Random Forest Classifier, a powerful ensemble learning method known for its accuracy and robustness in classification tasks. The model is trained on a comprehensive dataset encompassing various health metrics associated with CKD. By analysing this data, the classifier predicts the likelihood of a user developing CKD based on their input, enabling early detection and timely intervention. In addition to predicting CKD risk, the application utilizes K means clustering to categorize users into distinct stages of CKD, based on their health data patterns. This clustering approach aids in providing a clearer understanding of an individual’s kidney health status, which is essential for appropriate treatment planning and management. Furthermore, to enhance user experience and support proactive healthcare decisions, the application employs the K Nearest Neighbors (KNN) algorithm to offer personalized recommendations for nearby hospitals. This web application is designed to predict chronic kidney disease (CKD) through an intuitive user interface that enables individuals to securely log in and input their health details. The application employs a robust data processing pipeline to ensure the reliability of the predictions. Initially, it utilizes interpolation techniques for data cleaning, addressing any missing values in the user input to enhance dataset integrity. This is crucial, as incomplete data can lead to inaccurate predictions and hinder effective analysis. To facilitate the handling of categorical variables, the application incorporates Label Encoder, which converts these categories into numerical values that machine learning algorithms can process effectively.