Chronic Kidney Disease Prediction Using Deep Learning Classifiers

Mrs. T. Rubhasri; Dr. P. C. Senthil Mahesh

doi:10.32628/CSEIT2410225

Authors

Mrs. T. Rubhasri Department of Computer Science and Engineering, Excel Engineering College, Tamil Nadu, India Author
Dr. P. C. Senthil Mahesh M.E., Ph.D, Department of Computer Science and Engineering, Excel Engineering College, Tamil Nadu, India Author

DOI:

https://doi.org/10.32628/CSEIT2410225

Keywords:

Chronic Kidney Disease, Machine Learning, Deep Learning, Multi-Layer Perceptron Algorithm, Disease Classification

Abstract

Chronic Kidney Disease (CKD) or chronic renal disease has become a major issue with a steady growth rate. A person can only survive without kidneys for an average time of 18 days, which makes a huge demand for a kidney transplant and Dialysis. It is important to have effective methods for early prediction of CKD. Deep learning methods are effective in CKD prediction. Deep neural Network (DNN) is becoming a focal point in Machine Learning research. Its application is penetrating into different fields and solving intricate and complex problems. DNN is now been applied in health image processing to detect various ailment such as cancer and diabetes. In this project we can implement multi-layer perceptron algorithm to classify the chronic diseases with diagnosis information. Multilayer Perceptron is a Neural Network that learns the relationship between linear and non-linear data. The Multilayer Perceptron was developed to tackle this limitation. It is a neural network where the mapping between inputs and output is non-linear. A Multilayer Perceptron has input and output layers, and one or more hidden layers with many neurons stacked together. And while in the Perceptron the neuron must have an activation function that imposes a threshold, like ReLU or sigmoid, neurons in a Multilayer Perceptron can use any arbitrary activation function. Based on this function, we can identify the chronic kidney disease from the datasets which is downloaded from KAGGLE website. Experimental results shows that the proposed system provide improved accuracy in disease prediction.

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