Prediction of Chronic Kidney Disease using Deep Belief Network

Chronic Kidney Disease is a prevalent and weakening medical condition with a substantial impact on public health. Early detection and prediction reduces its adverse effects and improve patient outcomes. In this project, wepropose a comprehensive predictive analysis framework utilizing advanced machine learning algorithms to forecast the onset and progression of the disease. The dataset comprises a diverse range of clinical, population, and laboratory features collected from a group of chronic kidney disease patients over a defined period. Preprocessing techniques are employed to handle missing values and normalize data. Subsequently, various machine learning algorithms, including Random Forest, Support Vector Machine, and Gradient Boosting , are trained on the processed dataset to learn complex patterns and relationships within the data. Additionally, feature importance analysis is undertaken to identify the most influential variables contributing to prediction accuracy. The machine learning algorithms can effectively aid clinicians in predicting chronic kidney diseases, enabling timely interventions and personalized treatment plans. This research contributes to the growing knowledge on the application of machine learning in healthcare, particularly in chronic disease prediction.