Optimal design of quadratic support vector machine based end stage renal disease predictor model

Abstract

At present kidney failure has become one such important paradigm for higher mortality rate. Chronic kidney disease (CKD) is a severe kidney infection, and it has different stage of infection with final condition is being called as End Stage Renal Disease (ESRD). At this juncture patient are supported with dialysis or kidney transplant. The process requires time to analyze, diagnose the different parameters in determining CKD. The study involves using machine learning techniques for predicting the stages involved in infection and prevent the disease progression. There are four different stages of kidney disease, patient above 12 years of age are analyzed with 24 different input parameters. Around 160 subject’s parameters were taken into the database. Important feature includes serum creatinine, blood pressure, bacterial infection, pedal edema, urine sugar and sodium level in the blood are included. Machine learning algorithm was developed using MATLAB 2018 to predict the early end stage of CKD. Algorithms like Fine Decision Tree (FDT), Quadratic Support Vector Machine (Quad-SVM) and Linear Discriminant Analysis (LDA) are the three main algorithms used for designing the predictor model. Classification Learner Toolkit helps is determining the accurate model and Curve Fitting Toolbox helps in curve fitting over the given data set. After analyzing the system with different predictor model, the model with high accuracy and less training time is chosen. The proposed work also incorporates an e-message alert system linking the nephrologists in case of any emergency wirelessly. It enables high speed prediction and diagnosis process for treatment of subject under high risk factor. The threshold value is continuously monitored and in case if the value exceeds the safer limit, a notification is sent to the physician in proper time.