Intelligent Prediction of CKD Progression Using Ensemble and Deep Learning Methods

Abstract: This paper presents a flexible and an inexpensive chronic kidney disease prediction system by utilizing machine learning models including Deep Neural Networks (DNN), Support Vector Machines (SVM), and Extreme Gradient Boosting (XGBoost). The interface between the clinical data sets and advanced AI algorithms for accessing patient records and controlling disease progression remotely will be made by using comparative analysis of these three models. This study node connected to clinical attributes that can be controlled using smart data preprocessing and remotely controlled through an access point. The Smart CKD prediction system for healthcare development consists of two major parts that are smart diagnostic device and the access point. The main hardware for this system contain: Clinical Dataset, Machine Learning Models, Feature Selection, Data Preprocessing, Model Evaluation Metrics, Performance Analysis, Confusion Matrix, ROC Curves, and Statistical Analysis. Expected outcomes from this system: programming by using Python that comes built-in with Scikit-learn, TensorFlow module adapter to make connections between the clinical data and AI models for precise CKD prediction.

Keywords: Chronic Kidney Disease, Diagnosis, Deep Neural Networks, Support Vector Machines, XGBoost, Machine Learning, Artificial Intelligence, Clinical Decision Support Systems, Feature Selection, Early Detection, Health-care Analytic s, Accuracy, Sensitivity, Specificity.