A Novel Method for Predicting Kidney Disease using Optimized Multi-Layer Perceptron (PKD-OMLP) Classifier

Kidney diseases are commonly viewed among people. Medical analysis of Chronic Kidney Disease (CKD) is performed with a blood test and urine test. In recent times, data mining and analysis concepts are implied for predicting CKD through the application of patient details and recorded data. At this moment, predictive analysis modeling such as Support Vector Machine (SVM), Multilayer Perceptron (MLP), Linear Regression (LR) and proposed Optimized Multi-Layer Perceptron (PKD-OMLP) is executed for predicting CKD. Pre-processing is employed for reducing the level of misplaced data and impure data. During the processing stage, the identifiers are spotted which aid in the model forecasting. The selected three types of predictive algorithms are assessed and appraised relying on their prediction accuracy, precision values, and recall. The research study provides a decision-making tool that supports the forecasting of kidney diseases. The main goal of the study is to recognize CKD diseases at an earlier stage with the assistance of Machine Learning (ML) models like Linear Regression (LR), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP). In this study, models are designed with the use of Python programming with Python 3.7.0 and their performance is contrasted concerning the recall, accuracy rate, and precision. Among the preceding four models PKD-OMLP gives the best outcome as per its performance level producing accuracy of about 94.75%, precision of about 0.93 and recall of about 0.92 respectively on testing CKD dataset from Kaggle.